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Unqua Corinthian Yacht Club

Unqua Corinthian Yacht Club, located in Amityville, New York on the Great South Bay, is a private membership club dedicated to activities including day boating, cruising, competitive sailing, recreational and competitive swimming, and fine dining and entertainment.

Spectacular Views

Our clubhouse provides spectacular views of the Great South Bay while enjoying your dining experience.

Our Olympic Swimming Pool, fully renovated in 2022, provides an excellent venue for our swim team and enjoyment of our Members.

The Heading

Our Beach Station, known as “The Heading” is across the Bay on the North Side of the barrier beach at West Gilgo provides dockage and electric for our intrepid overnight boaters.

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Unqua Corinthian Yacht Club

Play in the heart of downtown baltimore, marina info.

Unqua Corinthian Yacht Club is based at Unqua Place in Amityville, New York. Unqua Corinthian Yacht Club has not been reviewed by any seafarers, be the first to review and rate this marina! Unqua Corinthian Yacht Club offers direct passage to the water and other amenities within Amityville. Contact Unqua Corinthian Yacht Club at 516-691-6570.

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Unqua Corinthian Yacht Club

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Amityville, NY 11701

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This place has nice views and it's a nice place to hang out (you must be a member.) However, dear god the food is horrible. I have gone multiple times with friends and it is literally some of the worst food I've ever had out. Any seafood I've ever had is like chewing on a tire (besides shrimp in the tacos) and the food is insanely overpriced and bland. Nothing is seasoned, macaroni and cheese is literally watery milk in overcooked pasta. Not worth the exuberant prices whatsoever.

Nice spot for a special dinner or event. The views of the bay are lovely. Food is very solid as is the service. I tried the burger with a Caesar salad. The burger was good but the salad was overdressed and wilted. Others were happy with their entrees- salmon, fried chicken, and steak.

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Unqua Corinthian Yacht Club, New York, United States

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George Ellis

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31 Unqua Pl

631-691-6570

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[email protected]

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Standard industrial classification code:		7999
City / suburb:		Amityville, NY
County:		Suffolk

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data based problem solving model

Data-based decision making.

Paper with bar charts on a table with a pen

What Is Data-Based Decision Making?

Data are an integral part of PBIS implementation, woven throughout every practice and system across every tier. School teams who use data to make decisions about student challenges are more effective and efficient than teams who don’t include data in their process. In PBIS, the data used most frequently fall into three categories: implementation fidelity, student outcomes, and screening. The first step to using data to make decisions is to figure out which questions teams want to answer. Once they have these questions, they can figure out which data to collect.

Implementation Fidelity Data

Teams rely on fidelity data to assess and monitor how closely adults implement practices the way they were intended to be implemented, and whether the systems supporting these practices are in place.When used in conjunction with student outcome data, implementation data also inform teams whether practices match student needs.

Student Outcome Data

Most schools and districts implementing PBIS focus on outcomes such as office discipline referrals, suspensions, school climate (as reported by staff, students, and parents), attendance, and academic performance. The student outcome data teams use will depend on the questions they look to answer.

Screening Data

Screening data help schools identify which students could benefit from additional supports. Universal screeners give a school-wide picture of how all students are doing – which students are progressing and which students are having more difficulty. Additional assessments and progress monitoring help teams pinpoint a students’ risk and choose solutions that match with students’ needs.

Why Use Data to Make Decisions?

Data provide educators with an objective way to assess how well they are improving student outcomes. Data help everyone identify strengths to build upon for increasing success. For all students to achieve social and academic success, teams must create systems that address equity and build cultural knowledge.

Foundational Elements of Data-Based Decisions

While there are many ways to incorporate data into a decision-making process, there are two foundational elements required:

Decision-focused data systems
Team-focused decision making

Decision-focused Data Systems

Collecting data isn’t hard. Finding efficient ways to report the data collected is more important. Entering data into systems with a focus on decision-making helps teams take advantage of their most limited resource:time.

Implementation Fidelity Data Systems

Teams assess their PBIS implementation fidelity regularly to be sure they continue to do what they said they would do. Teams can take these surveys on paper and manage the calculations on their own, or they can enter and report these data online in PBIS Assessment , a free, online application to do just that. Teams log in to PBIS Assessment and launch the survey they are scheduled to take. Once a team member or coordinator enters the data, they are immediately available to view in reports.

Discipline Data Systems

When it comes to making decisions about student behavior,office discipline referrals (ODR) are one piece of outcome data schools regularly collect. When it comes to ODRs, there are many data collection options available. As teams make decisions about which option will work best for them, there are a few recommendations to look for in a data system:

Date and time
Student name
Referring staff name
Student grade level
Perceived motivation
Others involved
Action taken
Data can be easily disaggregated by race and ethnicity
Efficient, up-to-date, accessible reports allow teams to create precision problem statements described in the team-based decision process below.

Team-based Decision Process

It’s possible to analyze data on your own, looking for trends, and implementing solutions. However, when tackled alone, you get a singular view of the data without some of the nuance. A team-based approach incorporates multiple perspectives and generates complete solutions.

An image of a lightbulb is used to symbolize the team-initiated problem solving process. The point of connection between the bulb and the electrical current consists of important meeting foundations like agenda and logstics. The light bulb is illuminated by the problem solving process which has the collection and use of data at the center of it.

Team Initiated Problem Solving (TIPS)

Watch overview video

TIPS is a research-validated framework to use during any team meeting focused on data-driven decision making . In the TIPS model, every team needs a minute taker , a facilitator, a data analyst , and at least one additional person available to be a backup to these roles if anyone is absent.

Identify the Problem with Precision

Watch Video

Teams looking at data are likely to come across discrepancies between expected performance and actual performance. To identify precisely what problem the team needs to solve, it needs to include:

‍ What is the problem you’re trying to solve? Disruptions? Reading fluency?
Where is the problem happening? ‍
When is the problem likely to occur? ‍
Who contributes to the problem most often? A few students? A specific grade level? ‍
Why does the problem seem to keep happening?

Identify a Goal

With a problem defined with precision, teams describe how they’ll know when a problem is resolved. What does success look like? When do you expect to see the problem resolved? Goals should be measurable so that teams will be able to say with clarity whether the problem persists.

Identify Solutions and Create a Plan

Based on the data teams have available, they next answer the question: What are we going to do? Solutions should fit the context of the problem. Solutions should include ideas for:

Prevention strategies
Teaching approaches
Opportunities to recognize desired behaviors
Ways to stop unwanted behaviors
Strategies to deliver consequences for unwanted behaviors

Whatever the solution teams identify, they need to document who will implement specific components, by when, and how to monitor its effectiveness over time.

Implement the Solution

Teams continually go back to the plan they created and checkoff the steps they said they would complete. This helps monitor the fidelity of the solution’s implementation. Some solutions may have associated assessments or checklists. Whatever teams do, they should know where they are in the implementation plan at all times.

Monitor the Solution’s Impact

In this phase of decision making, teams look to answer the question: Did it work? Teams go back to the data they collected to check whether they have met the goal, showed progress, or gotten worse. Measuring the impact ties directly back to the measurable goal teams set in the first decision-making steps.

Decide What to Do Next

At this point, teams need to determine how to proceed. Do they continue working toward the goal? Are there modifications they need to make to be more successful? Do we need to revise our goal to make it measurable or feasible? This is a refining step where teams make decisions together on how to move forward.

Tiers of Data-Based Decision Making

Common measures inform data-based decision making across all three tiers. Teams at each tier need to consider different levels of analysis (e.g., building level, classroom level, student level).

Tier 1 Teams review school-level data monthly to monitor the impact Tier 1 practices have on students. Based on these data, teams make adjustments as needed. Although district evaluation plans vary, many Tier 1Teams complete the Tiered Fidelity Inventory (TFI) one to three times per year and obtain yearly input and satisfaction information from students, families, and school personnel.

With Tier 1 systems in place, school teams should draft decision rules for identifying students who need additional supports. Data used as part of the identification process may include:

Office discipline referrals
Suspensions
Classroom minor behaviors
Instructional time lost
Academic performance
Attendance and/or tardies

Teacher referrals and systemic school-wide screening can also be used. Once students receive Tier 2 supports, teams review data every other week to monitor student progress. Additionally, schools can conduct the TFI to evaluateTier 2 systems fidelity.

Similar to Tier 2 considerations and decision rule strategies, data-based decision rules should be established to identify students who require Tier 3 supports. Likewise, data should be used to progress-monitor individual student plans. Annually, teams can conduct a TFI to assess Tier3 systems fidelity.

Get Started Using Data to Make Decisions

Adopt a discipline data collection system.

Schools and districts should use an electronic discipline data management system with the capacity to enter data, and to report data based on the team’s identified questions.

Useful discipline referral fields include:

Data can be easily disaggregated by race and ethnicity.

Collect Fidelity Data

Tiered Fidelity Inventory (TFI) — There are many tools available to assess a school’s overall PBIS implementation. The Center on PBIS recommend staking the Tiered Fidelity Inventory (TFI) , a research-validated measure to assess how closely school personnel apply the core features of PBIS. The TFI includes three separate surveys – one for assessing each tier. Use each survey separately or in combination with one another. Schools at every stage of implementation may use the TFI to assess anytier.

Additional surveys include:

‍ School Climate Survey (SCS) – to measure student, staff, and family perceptions of school climate
Benchmarks of Quality (BoQ) – to measure in detail the team’s perspective on Tier 1 strengths, weaknesses, and overall implementation

Create a Standardized Team Meeting Agenda Template

Agendas for team meetings need to incorporate a data-based decision making process to address implementation priorities. Within the TIPS framework, the meeting minute worksheet serves this purpose. An example template is available to download and adapt to fit your team needs.

Resources in this section include assessments, blueprints, examples, and materials to aid in implementing PBIS.

Publications

Publications listed below include every eBook, monograph, brief, and guide written by the PBIS Technical Assistance Center.

Presentations

Presentations about their experiences, published research, and best practices from recent sessions, webinars, and trainings

Recordings here include keynotes and presentations about PBIS concepts as well tips for implementation.

This website was developed under a grant from the US Department of Education, #H326S230002. However, the contents do not necessarily represent the policy of the US Department of Education, and you should not assume endorsement by the Federal Government. Project Officer, Mohamed Soliman.

Education & Teaching
Schools & Teaching

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Untangling Data-Based Decision Making: A Problem-Solving Model to Enhance MTSS (A practical tool to help you make sense of student data for effective use in MTSS)

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Untangling Data-Based Decision Making: A Problem-Solving Model to Enhance MTSS (A practical tool to help you make sense of student data for effective use in MTSS)

This book offers knowledge, strategies, and tools that will help you use data effectively. Applicable to any content area, the four simple steps in this book’s problem-solving model include specific questions that will guide your use of data to identify and solve problems that stand in the way of student achievement. Learn how to change instruction, curriculum, and environment to better support students.

Understand the proper application of data-based decision making
Successfully utilize data to improve student learning
Use worksheets and tools to organize data team meetings
Become data rich and information rich
Develop an MTSS within their school or district
ISBN-10 1943360782
ISBN-13 978-1943360789
Publisher Marzano Resources
Publication date February 15, 2024
Language English
Print length 264 pages
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Editorial Reviews

“This is the MTSS book I've been waiting for! Using the problem-solving model as the ‘engine’ of MTSS, the authors provide a comprehensive explanation of the relevant variables that impact learning, as well as the practical tools required for building effective systems of behavioral and academic support for all students. This book will empower educators with accurate and actionable information to counteract the many misunderstandings about MTSS.” -- Stephanie Stollar , assistant professor, Mount St. Joseph University; founder of the Reading Science Academy

“The use of data is a fundamental component of MTSS. The authors provide practical strategies for educators to effectively use data to inform their actions. MTSS team members at the schoolwide level to the individual student level will find this book useful in promoting meaningful outcomes for each and all students. A must read for those implementing MTSS within schools.” -- Steven Goodman , research specialist, Department of Educational Psychology, University of Connecticut

“This book includes the critical components for effective data-based decision making with tons of resources and examples to support practitioners. Just as I would think to myself ‘I hope they include this,’ the information would be there! If you’re just beginning to implement data-based decision making, or fine tuning your processes, this is the book for you. I think this book would be great for pre-service teachers, too, and look forward to using it in one of my undergraduate courses.” -- Erica Lembke , professor of special education, University of Missouri

About the Author

Jason E. Harlacher, PhD , is a senior researcher with American Institutes for Research and the director for the Center on Multi-Tiered System of Supports (MTSS Center; https://mtss4success.org). Jason began his career working with youths in a day treatment center, which sparked his passion for using data to create safe and effective school environments. Since then, he has worked at the state, district, and school levels, having held roles as a school psychologist, district-level technical assistance provider, adjunct professor, and state-level consultant. With over eighteen years in education, he presents across the United States on topics related to classroom management, data-based decision making, and MTSS. He is published in peer-reviewed journals and has authored several books, including Bolstering Student Resilience: Creating a Classroom With Consistency, Connection, and Compassion and An Educator’s Guide to Schoolwide Positive Behavioral Interventions and Supports: Integrating All Three Tiers . He earned a bachelor’s degree in psychology from Ohio University, a master’s in school psychology from Utah State University, and a doctorate in school psychology from the University of Oregon.

Adam Collins, PhD , is the founder of Envision Zero Bullying and author of Effective Bullying Prevention: A Comprehensive Schoolwide Approach . He has over fifteen years of experience researching and implementing bullying prevention best practices at the school, district, university, and state levels. Adam serves as the statewide bullying prevention manager at the Colorado Department of Education, where he spearheaded the creation of Colorado’s first statewide bullying prevention model policy and now leads a multimillion-dollar grant-funded bullying prevention program. He earned a bachelor’s degree in psychology from the University of Kansas and a doctorate in school psychology from the University of Nebraska–Lincoln.

Jon Potter, PhD , is a senior technical assistance consultant with American Institutes for Research. Jon has worked in education for more than sixteen years, supporting school districts in developing and sustaining MTSS. As an RTI implementation coach for the Oregon Response to Instruction and Intervention Project, he supported school districts across the state of Oregon in improving systems of reading instruction for all learners. He presents across the United States on topics related to making data-based decisions, developing positive school cultures that can sustain systems change, and translating the science of reading into effective classroom practices. He earned a bachelor’s degree in psychology from the University of Denver, and a master’s degree in special education and a doctorate in school psychology from the University of Oregon.

Product details

Publisher ‏ : ‎ Marzano Resources (February 15, 2024)
Language ‏ : ‎ English
Perfect Paperback ‏ : ‎ 264 pages
ISBN-10 ‏ : ‎ 1943360782
ISBN-13 ‏ : ‎ 978-1943360789
Item Weight ‏ : ‎ 1.6 pounds
#329 in Education Assessment (Books)
#555 in Education Administration (Books)
#9,626 in Unknown

About the authors

Jon Potter, PhD, has worked in education for more than 20 years, supporting school districts in developing and sustaining MTSS. He currently works as a senior technical assistance consultant with American Institutes for Research, providing technical assistance for a variety of projects including the Center on MTSS and the National Center on Intensive Intervention (NCII). He has previously worked as a school psychologist, and adjunct faculty member, and a statewide RTI implementation coach. He presents across the United States on topics related MTSS including making data-based decisions, developing positive school cultures that can sustain systems change, and translating the science of reading into effective classroom practices.

Jason E. Harlacher

I am a systems-level educator with expertise in the problem-solving model, multi-tiered system of supports (MTSS), and classroom management. Currently, I work as a Senior Researcher for the American Institutes for Research (AIR), where I serve as a project director with various departments of education and as a national trainer for the MTSS Center (mtss4success.org). With over 18 years of experience in education, I have worked as a school psychologist, district-level technical assistance provider, state consultant, researcher, and adjunct professor. I received my BA in psychology from Ohio University, MS in school psychology from Utah State University, and my doctorate in school psychology from the University of Oregon (go Ducks!).

Adam Collins

Adam Collins, PhD, is the founder of Envision Zero Bullying and sits on the Board of Directors for Act to Change, the only national non-profit dedicated to ending bullying for Asian American and Pacific Islander youth. Currently, Dr. Collins serves as the Statewide Bullying Prevention Manager at the Colorado Department of Education. He has extensive experience researching and implementing bullying prevention best practices at the school, district, university, and state levels. He regularly presents at state and national conferences on bullying prevention and MTSS.

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School of Psychology

Training Model: The School Psychologist as a Data-based Problem-Solver

Page content, primary objective.

The primary objective of School Psychology training at USM is to prepare behavioral scientists who can apply their skills to the solution of a broad range of problems related to the processes of schooling. Generalized empirically-based problem-solving skills represent the program's primary emphasis and are seen as essential in order for graduates to assume the diversity of roles associated with School Psychology today and in the future. All training in School Psychology is based on the scientist-practitioner model with special emphasis on integration of the scientist and practitioner dimensions.

The goal of integrating the science and practice dimensions of the scientist-practitioner model is approached through incorporation of an additional training scheme designated as the Data-Based Problem-Solver model (DBPS). One of the program's primary training objectives is to produce school psychologists who approach their professional activities from a cohesive frame of reference: that of a Data-Based Problem-Solver. The DBPS model teaches students to view all school psychological functions from a problem-solving perspective requiring systematic progression through the steps of (a) problem identification, (b) problem solution, and (c) problem evaluation. In addition, the DBPS stresses the importance of basing hypotheses and conclusions at each step on empirical data.

To facilitate communication..

Training in the Science area

The Practice area

Entry level preparation

Education dimension

The Education dimension includes coursework and experience related to: (a) Professional Issues, (b) Scientific Methodology, and (c) Theory and Data Bases. Since School Psychology is viewed as a sub-specialty of generic Psychology, training in these areas is provided relative to both the general discipline of Psychology and the School Psychology specialty area. The study of Professional Issues familiarizes students with the major professional organizations and their contributions to the discipline of Psychology and the profession of School Psychology.

Training elements in the Professional Issues area include: professional organizations, standards, ethics, and credentialing; and an introduction to School Psychology. The study of Scientific Methodology provides students with an empirical orientation and the skills to critically evaluate and contribute to the literature of Psychology and School Psychology. Training elements in the Scientific Methodology area include: research design and methodology, scientific writing, research in school psychology, and research participation including thesis and dissertation development. Study in the Theory and Data Bases area provides students with experience in the identification and critical evaluation of theory and data. Specific training elements in the Theory and Data Bases area include: biological and social bases; normal and abnormal development; and educational foundations.

Training dimension

The Training dimension represents supervised experience in the application of problem-solving skills to problems encountered by school psychologists in the schools. Supervised field experiences begin during the first semester of training, promoting an early integration of theory and practice, and continue throughout the program. The specific training elements associated with the Training dimension are organized around the problem-solving components of: (a) problem identification, (b) problem solution, and (c) problem evaluation. Other School Psychology training programs often label similar components as: (a) assessment, (b) intervention, and (c) evaluation. Use of the more general problem-solving terms reflects a conviction that general rather than specific problem-solving strategies are necessary to adequately prepare students to assume the variety of roles expected of school psychologists today and in the future. While the acquisition of specific problem-solving skills associated with the typical roles of school psychologists today constitutes a major focus of field training, students also gain experience in the use of their generalized skills to acquire new information and problem-solving strategies to develop solutions appropriate to the unpredictable problems encountered in the field. Under the supervision of program faculty, students representing each year level work together in teams providing supervisory experience for advanced students and allowing for observational learning by beginning students.

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Using a Problem-Solving Model to Enhance Data-Based Decision Making in Schools

Cite this chapter.

Stephen J. Newton ,
Robert H. Horner ,
Robert F. Algozzine ,
Anne W. Todd &
Kate M. Algozzine

Part of the book series: Issues in Clinical Child Psychology ((ICCP))

10k Accesses

16 Citations

Making decisions is a core activity in schools. Every school has faculty teams that meet regularly to make decisions concerning logistical, administrative, academic, and social issues. The thesis of this chapter is that team decisions will be more effective and efficient when they occur in the context of a formal problem-solving model with access to the right data, in the right format, at the right time.

We focus in this chapter on problem solving and data-based decision making related to behavior support in schools because that is where our experience has greatest depth. The principles and practices regarding problem solving and data-based decision making about behavior support, however, also extend to academic achievement and other areas of support. Themes emphasized throughout this chapter are that data-based decision making (a) occurs in the context of team meetings with a “structure” that sets the occasion for effectiveness; (b) is embedded in a formal problem-solving model with processes that ensure a meeting is logical, thorough, and efficient; and (c) is continuously informed by accurate and timely data.

An erratum to this chapter is available at http://dx.doi.org/10.1007/978-0-387-09632-2_30

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From “Intuition”- to “Data”-based Decision Making in Dutch Secondary Schools?

Data-Based Decision-Making, the Problem-Solving Model, and Response to Intervention in the Minneapolis Public Schools

Getting assessment right at the classroom level: using formative assessment for decision making.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

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Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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BP7, Vol. 1: Data-Based and Collaborative Decision Making

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Patti L. Harrison, Sherrie L. Proctor, and Alex Thomas

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Following the framework for the 2020 NASP Practice Model , Volume 1: Data-based and Collaborative Decision Making features 24 chapters focused on Domains 1 and 2.

Experts review best practices in:

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In addition, numerous chapters focus on assessment, including best practices in: nondiscriminatory assessment, assessment of bilingual learners in schools, teleassessment for children and youth, assessment of children's communication disorders, applying multiple models to assessment and identification of learning disabilities, school-based autism assessment and identification, and multimethod assessment of attention deficit hyperactivity disorder.

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Purpose of the Model

Philosophy of problem solving, fun: the bookworm, quick links.

This newsletter introduces the Problem Solving Model. This is a ten-step model to guide you (and your team) through a structured problem solving process. All too often, people jump from a problem to a solution. And it is often a solution that is short-lived or creates numerous other problems within the organization. The Problem Solving Model provides you a road map to continuous improvement.

As its name implies, this model is the road map to follow to solve problems. What makes something a problem?

a) When the process isn’t doing what it is supposed to and people don’t know why. b) When things keep going wrong no matter how hard everyone tries. c) When everyone believes that there is a problem to solve.

The first step in the model is to define the problem; it does not matter if it is late shipments, stock outs, computer downtime, typos, lost messages, or an agreed upon “red bead” that everyone keeps running into. Before you can solve the problem, you must truly understand what it is. This means brainstorming about the process, using a Pareto Diagram to prioritize potential obstacles and creating a process flow diagram of what is currently going on. After you have the problem defined, the model leads you through analyzing data you gather about the process, determining the root cause of the problem, and identifying possible solutions to the problem. Solutions to the problem will either be changes to the process which eliminate special causes of variation or changes which reduce common cause variation. After the best solution is implemented, the model leads the team to monitor the impact of its revisions to make sure that the problem is truly solved.

The problem-solving model, introduced below, incorporates an effective set of skills into a step-by-step process. The model combines the use of statistical tools, such as control charts and process flow diagrams, with group problem-solving skills, such as brainstorming and consensus decision-making. The statistical tools help us make data-based decisions at various points throughout the model. The group problem-solving skills help us draw on the benefits of working as a team.

Before we begin a discussion about the steps of the problem-solving model, we should talk a little about the philosophy that good problem solvers have about problems. Here are a number of ideas that are part of the philosophy.

Problem solving should occur at all levels of the organization. At every level, from top to bottom, problems occur. Everyone is an expert in the problems that occur in his or her own area and should address these problems. Problem solving is a part of everyone’s job.

All problems should not be addressed with the same approach. There are some problems that are easily and suitably tackled alone. Not all decisions need to be made by teams nor do all problems need to be solved by groups. However, groups of people help to break mental sets (i.e., figuring out new ways of doing things). In addition, people are more committed to figuring out and implementing a solution to a problem if they are involved in the problem solving.

Problems are normal. Problems occur in every organization. In excellent companies people constantly work on solving problems as they occur. Problems are opportunities to make things better and should be viewed as such.

Be hard on the problem and soft on the people involved. When working on a problem, we should focus on solving the problem, not on whose fault the problem is. We should avoid personalizing the problem and blaming others.

People should address the problems in their own areas. Everyone has problems associated with their work area, and they should take ownership for trying to solve these problems instead of waiting for their supervisors or another team to tell them what to do.

Problem Solving Model

Step 1: define the problem..

Step 1 is a critical step; it determines the overall focus of the project. In this step, the team defines the problem as concretely and specifically as possible. Five SPC tools are helpful in defining the problem: brainstorming the problem’s characteristics, creating an affinity diagram, using a Pareto chart, creating an initial Process Flow Diagram of the present process, and Control Chart data. The process flow diagram (PFD) will help the team identify “start to finish” how the present process normally works. Often the PFD can dramatically help define the problem. After the problem is well defined, Step 2 helps the team measure the extent of the problem.

End Product = A clear definition of the problem to be studied, including measurable evidence that the problem exists.

Step 2: Measure the Problem.

Baseline data are collected on the present process if they do not already exist. This permits measurement of the current level of performance so future gains can be subsequently measured. The team needs to make a decision on how to collect the present baseline data. In general, if data are collected daily, the time period should be a month. This way a standard control chart can be used. If data are collected weekly or once a month, baseline data will have only three or four points. Data collected less than once a month are of limited use; in such cases, historical data, if available, should be used. At this stage, the team must have measurable evidence that the problem exists. Opinions and anecdotes are a sound place to start, but eventually there needs to be concrete proof that there really is a problem.

End Product = A graph or chart with present baseline or historical data on how the process works; a collection of the present job instructions, job descriptions, and SOPs/JWIs (standard operating procedures and job work instructions).

Step 3: Set the Goal.

Goals provide vision and direction and help the team make choices and know which path to take. Be sure to state your goal(s) in terms that are measurable. This way, the team can evaluate its progress toward the goal. As the team imagines the goal, it will identify benefits of achieving the solution to the problem. This inspires a higher commitment and support from all.

End Product = A goal statement that includes the what, when, where, why, who and how of the ideal solved problem situation.

Step 4: Determine Root Causes.

In Step 4 the team studies why the process is working the way it is. If a control chart was developed in Step 2, determine whether the process is “in control” or “out of control.” If the process is “out of control,” the team should pinpoint the special causes and move to Step 5. If the process is “in control,” the team will need to use tools such as cause and effect analysis (fishbones), scatter plots and experimental design formats to identify root causes currently in the system producing common cause variation.

End Product = A list of most probable root causes of the problem (common and special cause variation); selection by team of the primary root cause of the problem to be eliminated.

Step 5: Select Best Strategy.

The purpose of Step 5 is to select the strategy that best solves the problem. From the list of causes generated in Step 4, the team should brainstorm and strategically plan solution strategies. Fishbone diagrams and benchmarking can be helpful for this step. Then the team must reach consensus on the best possible strategy to solve the problem. This strategy should have the highest likelihood of success.

End Product = A well defined strategy to solve the problem is selected.

Step 6: Implement Strategy.

An Action Plan is developed by team. This includes who will do what by when to implement the solution. The team sees to it that the Action Plan developed is carried out and documented.

End Product = The Action Plan is implemented.

Step 7: Evaluate Results.

In Step 7 the team evaluates how effective the solution has been. Data must be collected to determine if the implemented strategy did, in fact, improve the process being studied. Performance must be clearly measured and evaluated. The team needs to monitor control chart data where appropriate and assess improvement; the process flow diagram should be checked for appropriate SOPs and JWIs. Additional feedback strategies such as histograms, process FMEAs, customer surveys and informal polls may also prove useful. What are the “customer” reactions (internal customer feedback)? What has produced measurable results? What hard data are available? Do people perceive an improvement? How have results matched customer needs? If the process did not improve, the team needs to discover if the wrong root cause(s) was identified or if the wrong solution was utilized. In either case, return to the steps above, beginning with Step 4. If the process improves, but the results are disappointing, there may be other root causes affecting the process. Again, return to Step 4 to further examine additional root causes. When the problem is solved (i.e. the “loop closed”), the team proceeds to Step 8.

End Product = The problem is solved; results of the improvement are measured.

Step 8: Implement Appropriate Changes in the Process.

Step 8 develops an ongoing process to assure that the gains stay in place for the long term. Sometimes a problem is solved and then later resurfaces. This happens when a solution is determined, but a system or process to keep the problem solved has not been successfully adopted. Permanent changes need to be implemented. This means revising the existing procedures. The new improved process will need to be tracked over time; the process must be checked frequently to maintain improvement. This also helps everyone to stay aware of opportunities to continuously improve the process where the problem occurred.

End Product = A permanent change in the process, Quality Improvement, and people “closest to the job” monitoring the change.

Step 9: Continuous Improvement.

This step is staying committed to continuous improvement in terms of this model – to remain actively alert to the ways the improved process can be made even better. This step is a conscious decision to allow others to innovate and to point out “red beads” in the process which the team has worked hard to improve. All involved, particularly those closest to the job, need to be encouraged to give constructive feedback and adjustments. Internal audits will monitor some processes to ensure effectiveness.

End Product = Commitment to continuous improvement.

Step 10: Celebrate.

This last step includes a recognition celebration and the disbanding of the team. Always take time for this maintenance function; people have achieved an important goal. They have earned this moment of recognition and closure.

End Product = Closure for the team members; disbanding of the team.

Each of the four volumes in the picture has the same number of pages and the width from the first to the last page of each volume is two inches. Each volume has two covers and each cover is one-sixth of an inch thick.

Our microscopic bookworm was hatched on page one of volume one. During his life he ate a straight hole across the bottom of the volumes. He ate all the way to the last page of volume four. The bookworm ate in a straight line, without zigzagging. The volumes are in English and are right-side up on a bookcase shelf.

Challenge: how many inches did the bookworm travel during his lifetime? ____________

We will give the answer in next month’s newsletter.

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Open access
Published: 27 July 2024

Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy

Hongyu Wang 1 ,
Huicheng Hao 1 &
Mengdi Wang 1

Scientific Reports volume 14 , Article number: 17305 ( 2024 ) Cite this article

Metrics details

Applied mathematics
Engineering
Sustainability

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

Multi-objective green vehicle scheduling problem considering time window and emission factors in ship block transportation

Optimization of heterogeneous vehicle logistics scheduling with multi-objectives and multi-centers

Effective social spider optimization algorithms for distributed assembly permutation flowshop scheduling problem in automobile manufacturing supply chain

Introduction.

Driven by scientific and technological innovations and economic development, the world automobile market has been expanding rapidly in recent years, and the number of automobiles in the market has been rising gradually 1 . In addition, consumers' increasing demands for quality and innovative performance of automobiles have prompted automobile manufacturers to introduce more diversified model categories to meet the ever-changing market demands. This trend has forced the choice of materials, production processes, and quality control in the automobile manufacturing process to become increasingly demanding. Many of the reasons mentioned above ultimately lead to a significant increase in the number of faulty parts produced by users during the warranty period and the number of defective products produced by automotive companies during the manufacturing process, which are collectively referred to as automotive scrap parts in this paper. The automotive industry is a typical resource-intensive industry, and more than 90% of the steel and nonferrous metals on the parts are recyclable, which can bring considerable economic benefits 2 . Therefore, the recycling and remanufacturing of scrap parts materials have positive impacts on the return of funds to the automotive industry system and the implementation of the government's sustainable development strategy, and has become a research hotspot in the field of remanufacturing in various countries 3 , 4 . At the same time, the reverse logistics recycling process of scrap parts has also become an issue of great concern in the field of environmental resource protection and green logistics 5 .

For the world's largest automobile producer like China, the recycling of scrap parts for remanufacturing recovery is particularly urgent 6 . According to China’s National Bureau of Statistics (NBS), as of the beginning of 2023, China’s civilian ownership of automobiles amounted to 319 million, ranking first in the world. In recent years, the scrapping rate of automobiles is about 3.5%, from which it can be approximated that the scale of China's automobile scrapping parts is gradually expanding, as shown in Fig. 1 . The Chinese government has also launched a number of policies around 2020 to regulate the recycling mechanism of scrap parts and strengthen the guidance of the industry's sustainable development practices. At present, China's major automobile companies and their parts suppliers have strengthened their efforts to handle scrap parts to accelerate the return of capital and respond to the call for a sustainable development strategy.

Automobile scrapping trend in China.

To achieve technological spillover, reduce costs, and improve competitiveness, the automobile industry usually adopts the development mode of industrial clustering, which means that many automobile parts suppliers have set up manufacturing factories around the locations of automobile manufacturing enterprises. Therefore, the reverse logistics terminal for scrap parts usually has a corresponding logistics center to undertake the task of receiving scrap parts from vehicle manufacturing plants and automobile service stations around the world, and delivering them to the suppliers, as shown in Fig. 2 . The importance of logistics terminal distribution is becoming more and more prominent. However, as the number and diversity of scrap parts continue to increase, logistics centers are facing greater operational pressure, and many problems are gradually being exposed. Among them, the most common problems include the waste of resources by adopting a point-to-point mode for terminal distribution, the difficulty of coordinating the high inventory level, and the lack of scientific and reasonable planning for vehicle scheduling relying on the experience of drivers.

Reverse recycling logistics network.

In previous studies, experts and scholars on automotive reverse logistics have focused on scrap parts recycling policy 7 , 8 , reverse logistics network design and planning 9 , 10 , and reverse logistics network efficiency evaluation 11 , 12 . For the reverse logistics terminal of automobile remanufacturing, especially in the logistics terminal distribution for parts suppliers in industrial cluster areas, there are fewer researches in the past, and there is a lack of relevant theories and case studies. Therefore, the purpose of this paper is to establish a new model of terminal distribution of automotive scrap logistics that can be adapted to the context of sustainable development. To effectively realize the coordination between environmental cleanliness and enterprise cost-effectiveness, ease the inventory pressure of the automotive scrap logistics center, and promote the efficient operation of the distribution network. At the same time, we analyze the effectiveness of this study in actual operation to create a benchmark case and provide a case reference for the transformation and upgrading of the industry. The main contributions of the study are as follows:

In terms of model construction, this paper intervenes to analyze the terminal distribution mode of automobile scrap parts reverse logistics through practical cases. Under the background of sustainable development strategy, carbon emission, overall logistics costs, and delayed delivery rate are introduced as the optimization objectives, and the multi-trip green vehicle routing problem model with time windows (MTGVRPTW) is constructed.

In terms of algorithm design, this paper combines the nearest neighbor rule based on minimum cost, adaptive strategy, bin packing algorithm based on transfer-of-state equation, large-scale neighborhood search algorithm and genetic algorithm to design a hybrid adaptive genetic algorithm to solve the MTGVRPTW problem. At the same time, the adaptive genetic algorithm, genetic algorithm, and hierarchical particle swarm algorithm are used to conduct comparative experimental analysis to verify the validity and advancement of the algorithm.

In terms of factories location data collection, this paper proposes a coordinate extraction method based on image recognition technology that is feasible within a specific range, replacing the traditional method of converting latitude and longitude coordinates to plane rectangular coordinates, improving the efficiency of data collection.

This paper discusses the effectiveness of the proposed method in actual operation based on practical case study, which provides substantial reference value for the solution of reverse logistics terminal distribution problems in the industry.

The other sections are organized as follows. “ Literature review ” section reviews literature studies on related topics. “ Problem description and formulation ” section describes the problems in the case and constructs a mathematical model. “ Solution methodology ” section describes the design process of the solving algorithm. “ Computational experiments ” section discusses the numerical calculations of practical cases and simulation examples.

Literature review

As the concept of sustainable development continues to spread, the optimization and design of reverse logistics systems have attracted the attention of many scholars. Facing the optimization and design problem of the reverse logistics network of waste batteries in Turkey, Kilic et al. 13 proposed a two-stage multi-objective optimization method, achieving an effective combination of economic and environmental benefits. In terms of logistics network structure design, Sun et al. 14 focused on the e-commerce closed-loop supply chain network under uncertain environment. They used a robust point-to-point optimization method to establish a robust optimization model to reduce the negative impacts of uncertainties in forward and reverse logistics on the logistics network. Faced with the problem of reverse logistics of infectious healthcare waste in the context of the COVID-19 pandemic, Yaspal et al. 15 developed an optimization model considering cost-effectiveness and risk avoidance, using data-driven digital transformation to manage disposable medical waste.

The reverse logistics distribution problem studied in this paper essentially falls into the category of the vehicle routing problem (VRP), first proposed by Dantzig and Ramser 16 , which is an NP-hard problem. After conducting a literature review on related topics, we found that there has been limited research on the vehicle routing problem in reverse logistics. Regarding the recycling and reuse of recyclable waste, Cao et al. 17 studied the vehicle routing problem of a two-echelon collaborative reverse logistics network. Aiming to minimize total costs and considering constraints such as vehicle load, they established a heterogeneous electric vehicle routing model with time windows and designed an intelligent optimization algorithm for solving problems efficiently. For the reverse logistics system of urban sorted waste, Hong et al. 18 studied the joint decision problem of transfer station location and vehicle route planning. Their model considered greenhouse gas emissions and distribution costs, and they proposed a fast hybrid heuristic algorithm based on column generation and adaptive large neighborhood search, which effectively solved the problem. In the reverse logistics problem of kitchen waste, Shi et al. 19 studied the problem of the location of processing center and route planning, incorporating carbon trading policies into the model. Their scenario analysis of transportation capacity and methods concluded that the larger the capacity of electric trucks, the greater the economic and environmental benefits. Regarding the reverse logistics of construction waste, Chen et al. 20 focused on multi-depot vehicle routing problems with transport time windows of collision risk. They proposed cost-effective and environmentally friendly transport plans and developed an intelligent optimization algorithm for problem-solving. In considering dynamic energy consumption for multi-center mixed fleet reverse logistics distribution, Li et al. 21 conducted research on mixed fleet operating costs, charging station insertion strategies, and algorithm design. Their method demonstrated outstanding results in reducing total cost expenditure and improving average customer satisfaction across 15 sets of case experiments. Regarding the reverse logistics distribution of end-of-life electronic products in South Korea, Kim et al. 22 studied vehicle route planning operation modes with the objective of reducing transportation distance. They constructed a sub-vehicle routing problem for each regional center and designed a Tabu search algorithm for effective problem-solving.

A comprehensive review is conducted after combing through the relevant literature in the above research areas. First of all, from the perspective of research scenarios, there are fewer practical case studies on engineering applications, especially for the reverse logistics distribution of scrap parts returned to the factory for remanufacturing in automobile industry cluster areas, which have not been studied. Automobile scrap parts have high potential value, and there is an urgent need for academics to deal with this special scenario to provide the industry with a combination of theory and engineering case reference.

Secondly, regarding research models, previous studies have primarily focused on optimization objectives such as cost, carbon emissions, and customer satisfaction. However, the calculation methods for these objectives still need further study, particularly in addressing the mutual coupling issues among the objectives. In the VRP field, the design of optimization models often requires personalized analysis combined with specific application scenarios and cannot be generalized directly from different research contexts. The green distribution problem (GVRP) is particularly considered in this study, and there are a certain number of research results in the field of GVRP, which can provide a reference for the study of this paper. For example, for the green vehicle routing problem with simultaneous pickups, Olgun et al. 23 focused on reducing fuel consumption costs and meeting demand from customers in both pickup and delivery. They proposed a hyper-heuristic algorithm based on iterative local search and variable neighborhood descent heuristics to solve the problem. Regarding the green heterogeneous vehicle routing problem, Behnamian et al. 24 specifically considered the location and time of vehicle refueling while reducing carbon dioxide emissions, and designed a data mining-based firefly algorithm to solve the problem.

In addition, in terms of distribution mode selection, previous studies have considered multi-center and mixed fleet operation modes, but the integration of multi-trip distribution modes has not been adequately discussed. Assigning a single-trip delivery task to a single vehicle leads to substantial resource consumption, which is not conducive to achieving clean production. Some studies on multi-trip distribution are also worth noting. For example, inspired by urban waste collection practices, Huang et al. 25 introduced a new multi-trip vehicle routing problem with time windows and proposed a branch-and-price-cut algorithm to solve the problem. In addressing distribution issues in the beverage logistics industry, Sethanan et al. 26 considered multi-trip and heterogeneous fleet distribution modes. They proposed an integer linear programming formulation and a hybrid differential evolution algorithm combining genetic operators and a fuzzy logic controller.

Problem description and formulation

This section discusses the RT logistics center as a typical industry case study. Firstly, we analyze the original operation mode and the exposed problems. Then we propose an improved logistics terminal distribution mode. Finally, we construct a mathematical model based on the improved mode to solve the terminal distribution problem.

Describe the case and analyze the problem

The automotive scrap parts logistics center is responsible for receiving scrap parts from automotive service stations around the country and delivering them to the suppliers. RT logistics center mainly has the following functions: collection, sorting, inventory, storage and custody, and distribution. In the original distribution model, the RT logistics center adopts a point-to-point batch pickup model with suppliers, as shown in Fig. 3 . That is, when the suppliers' materials in the logistics center reach the set inventory level, the logistics center will send a pick-up notice to the supplier, and then the supplier will arrange its vehicle to pick up the materials and send them back to the factory. Through field research, we found the following problems in the logistics center under the original model:

The point-to-point bulk pickup model leads to high inventory levels, which results in expensive inventory costs. At the same time, excessive inventory takes up a large amount of storage space, making it difficult to adapt to future growth within the rapidly expanding automotive market.

The limited scale of the supplier's self-pickup model leads to high transportation costs and low logistics efficiency, resulting in high logistics costs and high levels of carbon emissions for the entire distribution network.

Since multiple suppliers are involved, it is difficult to coordinate the vehicle models and pick-up times of each supplier, this can easily lead to confusion in the management of the logistics center, interfering with the normal operation status of the outbound storage link.

Original distribution mode.

Designing improved distribution mode

Under the original distribution model, a direct way to reduce inventory levels was to increase the frequency of supplier pickups. However, the rational decision-makers of the suppliers, are not willing to bear more logistics and transport costs while their interests remain unchanged. Therefore, this paper decides to improve the logistics system from the perspective of changing the point-to-point transport mode to reduce the logistics inventory level and the overall logistics costs of the whole supply chain. It will also help to reduce the carbon emissions in the logistics process and support the implementation of green sustainable development policies.

The Milk-run model is a point-to-group efficient delivery method, which many scholars 27 , 28 have applied in production research. Studies have shown that the adoption of this model can reduce transport and inventory costs by increasing the loading rate of transport vehicles, thus achieving a reduction in the total costs of logistics. In view of its characteristics of “multi-frequency, small batch, and fixed time window”, it can be a better solution to the problems existing in the RT logistics center under the original model. Therefore, this paper decides to establish a kind of circular distribution network based on Milk-run with the logistics center as the leader, as shown in Fig. 4 , and then combines it into the MTGVRPTW for in-depth study.

Improved delivery mode.

Model assumptions and symbol description

Model assumptions.

In the actual transportation and distribution process, the vehicle will be affected by a variety of uncontrollable factors, so this paper makes the following assumptions about the mathematical model: (1) Sufficient capacity to take on the distribution needs of suppliers. (2) The transport process is not affected by weather, traffic control, travel peaks, etc., and always maintains the set average speed at an even pace. (3) After each trip delivery departs from the logistics center, it serves the customers in turn according to the optimization results and returns to the logistics center upon completing the delivery task. (4) Each distribution trip can serve multiple suppliers, but each supplier set cannot be delivered by multiple distribution trips. (5) Distribution vehicles are subject to the double limitation of carrying capacity and loading space, which cannot exceed the constraints limitations, and the loading space is expressed in the form of the number of loading units that can be loaded. (6) The vehicle stays at each site for the same time.

Symbol description

The symbols used in the MTGVRPTW model constructed in this paper and their related descriptions are shown in Tables 1 and 2 .

Decision variables

Mathematical model

Objective functions.

In the context of cleaner production and sustainable development strategies, when building the MTGVRPTW model for automotive scrap parts distribution, it is necessary to consider the impacts of various factors, including: (1) Reducing ecological impacts. (2) Achieving cost reduction and efficiency in logistics. (3) Ensuring the timely delivery of distribution services. On this basis, this study proposes three different optimization objectives.

Minimizing carbon dioxide emissions

The ecological impact of the vehicle distribution process is usually caused by the fact that driving a vehicle consumes fuel and produces carbon dioxide. Therefore, the first optimization objective in the model is set to minimize carbon dioxide emissions during the delivery process. Zhou et al. summarized most of the estimation methods on carbon emissions from automobiles 29 , considering the difficulty of obtaining data, this paper decided to use the fuel consumption rate measure to calculate. The specific formula is shown in Eq. ( 1 ).

Minimizing overall logistics costs

In the process of logistics distribution, a variety of transportation resources need to be deployed, which will generate several costs, including internal preparation, vehicle rental, driver labor, and distance transportation. The goal of the actual operation of the enterprise is to reduce the overall logistics costs and improve the efficiency of resource utilization. Therefore, this paper sets the lowest overall logistics costs as the second optimization objective. The specific formula is shown in Eq. ( 2 ).

Preparation costs for departure

Before the departure of each trip within the enterprise involved in the shelves, moving storage, loading and other logistics arrangements, including a large number of logistics equipment, material and human resources, the unified deployment. The costs of this item is shown in Eq. ( 3 ).

Vehicle rental costs

Distribution vehicles required for distribution are obtained by leasing with third-party companies, which generates vehicle leasing costs. The final decision on the number of vehicles to be rented is related to the result of combining multiple trips. The costs of this item is shown in Eq. ( 4 ).

Driver labor costs

Each vehicle is provided with a driver, who is employed on a temporary basis. If the actual delivery time of the vehicle m is less than the legal working hours of half a working day (4 h), a half-day contract is concluded with the driver of the vehicle; otherwise, a full-day contract is concluded. The costs of this item is shown in Eq. ( 5 ).

where CH m represents the labor costs of equipping the vehicle m with a driver and is related to the travel time of each vehicle. It is shown in Eq. ( 6 ).

where the formula for the travel time of each vehicle is as described in Eq. ( 7 ).

Vehicle transportation costs

Transportation costs will be generated when transporting goods, which include fuel costs, etc. And it is directly proportional to the distance traveled. The costs of this item is shown in Eq. ( 8 ).

Minimizing delayed delivery rates

If the delivery vehicle arrives at the destination earlier or later than the time window required by suppliers, it may disrupt the supplier's normal work situation. Therefore, we expect the delayed delivery rate to be minimized to avoid disrupting the supplier's work schedule due to unfavorable delivery. The specific formula is shown in Eq. ( 9 ).

where constraint Eq. ( 10 ) restricts the allocation of each site to only one trip. Constraint Eq. ( 11 ) limits the number of times a single vehicle can enter and exit the logistics center under multi-trip distribution to the same number of times. Constraint Eq. ( 12 ) restricts the number of times a vehicle can drive in and out of the same stop to remain equal. Constraint Eq. ( 13 ) restricts that no distribution task on each trip exceeds the volume limit. Constraint Eq. ( 14 ) restricts each distribution task on each trip to not exceeding the load limits. Constraint Eq. ( 15 ) limits the number of miles traveled by each carrier vehicle to no more than the vehicle's range limit. Constraint Eq. ( 16 ) restricts only vehicles that are in service to distribution duties. Constraint Eq. ( 17 ) restricts the order in which vehicles are put into service from m to m + 1. The constraint Eqs. ( 18 – 20 ) defines the decision variable as 0 or 1.

Multi-objective processing

There are three objectives of different properties in the optimization model. Considering the complexity of the solution and the decision preference, this study uses the weighted sum method to integrate these objectives into a single objective function 30 . Setting the weights of the three objectives as convex combinations, i.e., $\lambda_{1} ,\lambda_{2} ,\lambda_{3} \ge 0$ and $\lambda_{1} + \lambda_{2} + \lambda_{3} = 1$ . Due to the large number of members in a distribution network and the fact that distribution costs are shared by all members, decision-making must take into account the opinions of all members. Group-analytic hierarchy process (G-AHP) is a comprehensive evaluation method developed based on hierarchical analysis, which can effectively integrate the knowledge and experience of multiple experts for group decision-making 31 . Therefore, this paper will apply G-AHP to determine the weights of the three objective functions, drawing on the reviews of a team of experts consisting of representatives from logistics centers and suppliers.

Calculate the weight vector of each expert's judgment matrix

In the formulation of the weight standard, a total of 5 experts participate in the group decision, and the judgment matrix constructed by the review opinion of each expert are $A_{1} ,A_{2} ,A_{3} ,A_{4} ,A_{5}$ . The expression form of each judgment matrix is $A_{l} = (a_{ijl} ); \, i,j = 1,2,3; \, l = 1,2, \ldots ,5$ , where $a_{ijl}$ denotes the relative importance of factor i over factor j as perceived by the expert l . Separately solve their weight vectors $\tilde{w}_{il} \, = \,(\,\tilde{w}_{1l} ,\tilde{w}_{2l} \,,\tilde{w}_{3l} \,)^{T}$ , where $\tilde{w}_{il}$ represents the judgment weight value of the expert l for the objective function i . To ensure the consistency of the weight calculation results, a consistency check is performed, aiming for $CR_{l} = CI_{l} /RI_{l} < 0.1$ .

Calculate the group’s composite weight vector

In this paper, considering the fairness and balance, under the condition that $\sum {\lambda_{l} = 1,\quad (\lambda_{l} > 0,\quad l = 1,2, \ldots ,5)}$ , the weight of each review expert's opinion is set to be $\lambda_{l} = 1/5 = 0.2$ . Performing the weighted arithmetic mean calculation on the respective components of each weight vector, as shown in Eq. ( 21 ).

Following normalization of $\tilde{w}_{i}$ as Eq. ( 22 ), the weight vectors for the three objectives can be derived as $w_{i} = [0.164,0.539,0.297]^{T}$ .

To address the dimensional differences among $Z_{1}$ and $Z_{2}$ , the method of min–max normalization is employed to transform the multi-objective problem into a scalar optimization problem. Where $\underline{{Z_{1} }} ,\underline{{Z_{2} }} ,\underline{{Z_{3} }}$ are the lower bounds of $Z_{1} ,Z_{2} ,Z_{3}$ , and $\overline{{Z_{1} }} ,\overline{{Z_{2} }} ,\overline{{Z_{3} }}$ are the upper bounds of $Z_{1} ,Z_{2} ,Z_{3}$ . The lower and upper bounds are calculated by the box constraint. The transformed single objective optimization model is shown in Eq. ( 23 ).

Solution methodology

Vehicle routing problem (VRP) is NP-hard problem, which is usually solved using heuristic algorithms 32 . Genetic algorithm searches for optimal solutions by simulating the natural selection and genetics mechanism of Darwin's biological evolution theory, which has a strong global search ability and can achieve desirable optimization effects in solving VRP. However, because it is a stochastic search method, the local search ability is insufficient. Therefore, in this paper, a hybrid adaptive genetic algorithm (HAGA) is designed to enhance the solution quality and robustness of the solution algorithm by improving the genetic algorithm on initialization population, genetic strategy, and local search, respectively, with respect to the problem characteristics. For "the bin packing problem" generated by the multi-trip distribution of vehicles, this paper proposes a bin packing algorithm based on transfer-of-state equation to solve the problem.

Hybrid adaptive genetic algorithm

Coding method

Encoding is the process of mapping the solution space of a problem to a genotype representation in a genetic algorithm, allowing the algorithm to manipulate and evolve individuals. To increase the speed of model solving, this paper uses integer encoding.

Initialize population based on NNC rule

The classical genetic algorithm generates the initial population using a randomized method, resulting in a low degree of individual adaptation, which restricts the convergence speed of the algorithm. The nearest neighbor rule based on minimum cost (NNC) is a construction rule that produces higher quality feasible solutions, an idea first proposed by Solomon 33 . In this paper, we use the NNC rule to optimize the initial individuals and leverage its local optimization search to generate new individuals, aiming to improve the overall quality of the initialized population and accelerate the optimization process.

Initialize population based on NNC rule
Step 1	For a given departure time, start a distribution trip from the logistics center;
Step 2	Select the unreached site with the smallest "distance" from the current site, and insert this site into the route of the current trip if it meets the constraints;
Step 3	Repeat Step 2. If the relevant constraints are exceeded, a new distribution trip is added. If all sites are delivered, the calculation procedure is stopped

The “distance” between sites is defined as a weighted sum of the travel time between the two sites, the proximity of the time windows, and the urgency of the time window at the latter site. The “proximity of the time window” is the difference between the “start of service” at the latter site and the “completion of service” at the former site. The “urgency of the time window” of a site is the difference between the “latest service time” of the site and the “start service time” of the site. The “distance” between the two sites is shown in Eq. ( 24 ).

where c ij represents the "distance" between sites i and j . t ij represents the travel time from site i to j . T ij represents the proximity of the time window of sites i and j . E ij represents the urgency of the time window of site j . δ 1 , δ 2 , δ 3 represent the weighting coefficients, which satisfy δ 1 + δ 2 + δ 3 = 1.

Fitness function

The fitness function is used to measure the degree of adaptation of an individual in the problem space. The larger the value of the individual’s fitness, the higher the probability of remaining for the next generation of individual reproduction. For the minimization optimization model in this paper, the fitness is designed to be inversely proportional to the objective function, as shown in Eq. ( 25 ).

where Z denotes the objective function after transformation processing, as shown in Eq. ( 23 ).

Selection operation

The selection operator is a key step used to choose individuals for the next generation. Its main purpose is to select superior individuals from the current population based on their fitness values for subsequent operations. In this paper, tournament selection was used.

Adaptive scheme

The genetic algorithm is improved by using an adaptive genetic strategy, which adaptively changes the crossover and mutation probabilities according to individual fitness values, effectively avoiding local optima 34 . At the beginning of the genetic algorithm run, the individual differences are quite large. A higher crossover probability is chosen to increase the rate of new individual emergence, and a smaller mutation probability is chosen to speed up the convergence of results. In the later stages of the genetic algorithm, to reduce the probability of the algorithm falling into a local optimum, a smaller crossover probability should be used to protect well-adapted individuals, and a larger mutation probability should be used to increase the diversity of the population. In this way, the algorithm can jump out of the local optimum in time and determine the optimal or near-optimal solution in the shortest possible time. The following is the expression of crossover probability Eq. ( 26 ) and mutation probability Eq. ( 27 ) for the adaptive genetic operator proposed in this study.

where P c 1 is the set minimum crossover probability. P c 0 is the set maximum crossover probability. P m 1 is the set minimum mutation probability. P m 0 is the set maximum mutation probability. f avg is the current population mean fitness value. f max is the population maximum fitness value. f’ is the fitness value of the larger of the two individuals that crossover. f is the fitness value of the variant individual.

Cross operation

The crossover operator acts on the two paternal chromosomes to produce two new offspring individuals that contain the paternal genes but are different from the paternal chromosomes. In this paper, the OX crossover is used to speed up the operation and better preserve individual personality. The crossover operation process is shown in Fig. 5 .

OX cross operation.

Mutation operation

The mutation operator acts on one parent chromosome to make individuals in the population mutate, enriching the diversity of chromosomes within the population, improving the algorithm's ability to find the best, and preventing the algorithm from maturing prematurely. The mutation operation process is shown in Fig. 6 .

Mutation operation.

Local search operation

Large-scale neighborhood search algorithms (LNS) have the advantage of local search ability. Therefore, this paper leverages the core concepts of LNS, i.e., destruction and repair 35 , to design remove and reinserting operators to effectively compensate for the lack of local search ability of adaptive genetic algorithms. The remove operator refers to removing a portion of supplier sites from the current solution, while the reinserting operator refers to reinserting the removed supplier sites into the current solution. The schematic of local search is shown in Fig. 7 .

Schematic of the local search.

Remove operator

The removal operator is designed to identify distribution sites for removal based on the correlation value R . The removal operator operates as follows: Firstly, set the number p of sites to be removed, randomly select a site i from the current solution to be removed, and store the sites to be removed in the set S. Then calculate the correlation R between the remaining sites and the selected sites, and sort the correlations, select the site with the largest correlation for removal, and add it to the set S . The process is repeated until p - 1 destructive sites have been selected. The calculation of R is shown in Eq. ( 28 ).

where $D_{ij}{\prime}$ denotes the normalized distance value between sites i and j , which is calculated as in Eq. ( 29 ). $V_{ij}$ denotes whether site i and j are on the same routing or not, and is 1 if they are on the same routing, and 0 otherwise.

Reinserting operator

After removing a number of distribution sites using the remove operator, the removed distribution sites are then reinserted into the relevant locations on the routing using the reinserting operator, and the insertion is checked to see if the constraints are satisfied. The reinserting operator operates as follows: Firstly, find the best insertion position of each site in the set S that minimizes the increase of the objective function value in the post-destruction solution. Then calculate the target increase value of each site in S after inserting it to the best position, choose the site with the largest target increase value as the first insertion point, and repeat this operation until all sites in the set S are inserted into the destructed solution.

Bin packing algorithm based on transfer-of-state equation for solving multi-trip distribution

In the MTGVRPTW, which considers single-vehicle multi-trip delivery, the process of assigning trips to vehicles to obtain a solution is a typical "bin packing problem". The problem can be described as follows: there are a sufficient number of carrier vehicles ${\varvec{M}} = \{ 1,2 \ldots ,M^{*} \}$ , and the maximum operation time of the vehicle is $W$ . The distribution routings set ${\varvec{K}} = \{ 1,2 \ldots ,K^{*} \}$ of several trips is obtained after decoding each chromosome of HAGA algorithm, and the distribution time of each trip is $w_{k}$ . The task now is to design an algorithm that optimizes the allocation of distribution trips, aiming to minimize the number of vehicles required. The bin packing problem in this paper can be denoted by Eq. ( 30 – 34 ):

where Eq. ( 30 ) denotes that the objective of optimization is to use the minimum number of vehicles. Constraint Eq. ( 31 ) denotes that the total time of multi-trip distribution by each vehicle does not exceed the total working time of the sites. Constraint Eq. ( 32 ) indicates that all trips are carried by one and only one vehicle. Equations ( 32 , 34 ) defines the decision variable as 0 or 1.

In the solution of this problem, while the optimal solution can be obtained by using the exact solution method, the calculation process is more complicated. If the greedy algorithm is used, the results can be obtained faster, but the results are often not satisfactory. Therefore, this paper combines the exact solution method of dynamic programming with the greedy idea, and designs a combinatorial solution method based on transfer-of-state equation for solving the "bin packing problem" in the multi-trip distribution problem.

Bin packing algorithm program based on transfer-of-state equation
Step 1	Obtain the basic data $W$ and $w_{k}$, and initialize the number of vehicles to $m = 1$. Identify the trips which the single-trip delivery time $w_{k}$ exceeds $W$, remove them from the set, and count their number as $m_{0}$
Step2	Use dynamic programming to take out a number of delivery trips from the current "delivery trips set", and make them carried by -th vehicle, ensuring that the -th vehicle's operating time approaches Dynamic planning procedures: (1) Initialize a two-dimensional array, where $dp[i][j]$ denotes the maximum value that can be obtained by placing a vehicle with a maximum time in service of , considering the first distribution trips (2) Initialize $dp[0][j] = 0$,$dp[i][0] = 0$, where $i = \{ 1, \cdots ,K^{} \}$,$j = \{ 0,1, \cdots ,W\}$ (3) Use transfer-of-state equation to populate the array until all trips have been computed $dp[i][j] = \left\{ {\begin{array}{{20}c} {dp[i - 1][j]} & {,j < w_{i} } \\ {\max \{ dp[i - 1][j],dp[i - 1][j - w[i]] + v[i]\} } & {,j \ge w_{i} } \\ \end{array} } \right.$ (4)$dp[n][W]$ is the maximum value that can be obtained given the maximum time that a vehicle can be put into service, and the reverse derivation to find out the selected distribution trips
Step 3	Remove the delivery trips selected in Step2 from the "delivery trips set" and add a vehicle so that $m = m + 1$
Step 4	If the current "delivery trips set" is not empty, then skip to Step 2. If the current "delivery trips set" is empty, then end the calculation and skip to Step 5
Step 5	Output the total number of vehicles used $m^{*} = m_{0} + m$, and the distribution trips assigned by each vehicle

It is worth noting that the bin packing algorithm can effectively solve the multi-trip merging problem when all delivery sites share a unified time window. However, it may not be applicable when the time windows of different sites vary. That said, most manufacturing factories operate under a uniform work schedule, and reverse logistics deliveries are generally less urgent. Therefore, this method can be well-compatible within the industry.

The flowchart of the final solution algorithm for the problem model of this paper is shown in Fig. 8 .

The algorithm flowchart for solving the problem.

Computational experiments

In this section, we explore an application example scenario based on the MTGVRPTW model and the solution algorithm for the RT automotive scrap parts logistics center to assess the implementation benefits of the improved distribution model and the effectiveness of the solution algorithm. For those enterprises involved in more complex supply chains, which are located in industrial clusters with many suppliers, the distribution scale of reverse logistics terminals will be even larger. Therefore, this paper is oriented to medium and large-scale cases for simulation and analysis, aiming to make the research more universally applicable to the industry and to test the robustness of the algorithm, thereby deepening the significance of the research.

Numerical analysis of application example scenarios

To more intuitively reflect the superiority of the hybrid adaptive genetic algorithm (HAGA) designed in this paper, the adaptive genetic algorithm (AGA), genetic algorithm (GA), and hierarchical particle swarm algorithm (HPSO) are introduced to conduct the comparative experiments. The solution results are compared and analyzed, in terms of convergence characteristics and solution quality, to verify the robustness and optimality-seeking ability of HAGA.

Algorithmic parameter setting

In this paper, the parameters of the algorithms are set utilizing arithmetic tests and references to previous research experience, as shown in Table 3 . All the algorithms are implemented by MATLAB R2017a 36 programming, and the experimental results are output by running the software. The computer parameters are configured as Intel Core i5-12500H, 2.5 GHz, 16 GB RAM. The results of each algorithm are based on 20 runs.

Data collection and processing

Extract supplier site coordinate data

In previous studies, to collect the planar coordinates of the distribution sites, most of the previous researchers used the method of collecting the latitude and longitude data of the distribution sites from maps, and then converting the latitude and longitude coordinates to the planar rectangular coordinates by using various software such as MAPGIS 37 . In this paper, we argue that although the method above is feasible, it takes a long time to extract the coordinates when encountering large-scale practical cases. Therefore, this paper proposes a fast extraction method of planar coordinates based on image recognition technology that is feasible on a small scale.

The idea of the method is that when the distribution area under study is small, the idea of mathematical differentiation can be applied to approximate the sphere as a plane. The distribution area of this paper is 28 km × 24 km. The schematic diagram of this method is shown in Fig. 9 . The specific operational procedure is as follows:

Data preparation and original layer. Initial data collection is performed using the Google Maps service to capture a planimetric map of the area containing all suppliers and set it as Layer 1. The image must contain a clear scale, as it is a key element for precise distance calculations.

Creation and annotation of the calculation layer. Create a new transparent layer, Layer 2, above Layer 1. In Layer 2, use a graphical marking tool (e.g., dots) to accurately annotate the scale and supplier site locations, optimizing the target capture efficiency of the image recognition algorithm. This step aims to avoid loss or noise interference in subsequent data processing.

Image grayscale processing. Remove Layer 1. Convert the image of Layer 2 to grayscale to reduce image complexity and decrease the computational demand of the algorithm, thereby improving the accuracy of the image recognition process.

Image recognition and data extraction. Apply image recognition technology to identify specific grayscale points in the image, which represent the pixel locations of the distribution sites. Simultaneously, the algorithm identifies the pixel coordinates at both ends of the scale and calculates the pixel distance between the two ends, providing the necessary data support for coordinate conversion.

Coordinate conversion and calibration. Based on the pixel distance provided by the scale, perform a proportional linear transformation on the pixel coordinates of the distribution sites, converting them into actual plane calculation coordinates. This step ensures the accurate conversion from the image to actual geographic locations.

Computational coordinate extraction method based on image recognition. The Maps were captured on the Google Maps 38 platform ( https://www.google.com/maps ).

To verify the accuracy and reliability of the data collection method in this paper, the collected data are compared and analyzed with the measurement data from the Baidu Maps platform. Some of the comparison results are shown in Table 4 . The distance calculated by each site through the image recognition technology to extract the coordinates compared with the actual distance, the accuracy ranged from 95 to 99%, and the overall average accuracy of 98.88%. These results are within the acceptable range, proving the effectiveness of the method proposed in this paper.

The method has several advantages over traditional methods. The image recognition method significantly reduces the manual involvement in data collection. For large-scale data collection scenarios, this method can complete data extraction for an entire area within minutes, whereas traditional methods may take hours or even longer to achieve the same task. The time-saving advantage of the proposed method is particularly significant when facing the coordinate collection of multi-change scenarios.

In addition, in the case of natural disasters and other scenarios, emergency shelters are usually located in no fixed place, and it is more scientific and feasible to extract coordinates based on real-time remote sensing satellite images using image recognition technology in the distribution of emergency supplies.

Description of other data

The model of this paper and other data involved in the calculation of the elaboration of the description: (i) The generation of scrap parts is unpredictable, and the number of each time period varies. In order to optimize the allocation of resources, each delivery is made with a third-party company to reach a vehicle rental and driver temporary employment agreement, so this paper is not constrained by the number of vehicles and drivers. (ii) Considering the large number of suppliers, scrap parts belonging to the same supplier are loaded using standardized cargo units. Considering the road conditions within the city, this study focuses on leasing 6.8-m vans. The vehicle specification is 6800 mm × 2450 mm × 2600 mm, with double-layer palletizing, and a single truck with 24 cargo unit positions. The cargo unit required by each supplier for daily distribution can be calculated from the current data. (iii) Considering the realistic road conditions and other factors, the average speed of vehicle traveling was set to be 35 km/h. (iv) Preparation costs for departure is set to 50 yuan/trip. (v) In the actual distribution, the distribution site is not a straight path. To make the simulation closer to reality, we set a certain relaxation factor, which is calculated as follows: simulation distance = euclidean distance between coordinates × relaxation factor. After calculation, this paper takes the value of 1.6. (vi) Transportation cost per unit distance for distribution vehicles is 2 yuan/km. Conversion factor for carbon emissions and fuel consumption $e_{0}$ is calculated as 0.34L of diesel fuel for 1 kg of carbon dioxide. Vehicles with a maximum load capacity $Q_{K}$ of 8 tons. Fuel consumption per uni15t distance $\rho_{ok}$ at no load is 0.117 L/km. Fuel consumption per unit distance $\rho_{k}^{*}$ at full load is 0.377 L/km. (vii) The normal working time window for the logistics center and each supplier is 9:00 to 17:00, which is unified.

Practical case solving

In the new distribution mode, the number of automotive scrap parts faced at different delivery cycle intervals varies, requiring different transportation resources. In the original mode, the average shipping frequency of point-to-point delivery is about 4–5 days. One of the purposes of this study is to reduce the operational pressure of the logistics center and reduce the inventory level, so the delivery cycle interval is set to 1–3 days, respectively. To help enterprises better reach cost reduction and efficiency, this paper analyzes the target benefits under different distribution cycle intervals. Some of the example data are shown in Table 5 .

The model solution results and algorithm iteration process under different distribution cycle intervals are shown in Fig. 10 and Table 6 .

The optimal routing of the solution and the iterative process of the algorithm.

Result discussion

Discussion of results using different optimization algorithms.

From Table 6 , it can be seen that there are significant differences in the optimization effects of different solving algorithms. In terms of the performance of the overall objective function, HAGA > AGA > GA ≈ HPSO, and the optimized number of trips and the number of vehicles after solving the HAGA is less than those of the AGA, GA, and HPSO, regardless of the distribution cycle intervals. In terms of the performance of the objective function $Z_{1}$ , the HAGA is in the leading position, and its optimized carbon dioxide emissions are better than the AGA by an average of 7.46%, the GA by an average of 8.38%, and the HPSO by an average of 6.68% in different distribution cycle intervals. In terms of the performance of the objective function $Z_{2}$ , the HAGA is also in the leading position, and its optimized overall logistics cost is better than the AGA by an average of 3.64%, the GA by an average of 7.75%, and the HPSO by an average of 7.94% in different distribution cycle intervals. Meanwhile, in terms of convergence speed, the HAGA can enter convergence with fewer iterations, and outperforms the AGA by an average of 16.83%, the GA by an average of 20.27%, and the HPSO by an average of 20.85% in different distribution cycle intervals. Although the solution time of the HAGA is slightly longer than the other algorithms, its computation time of only a few minutes does not overburden the overall task and is within an acceptable range.

Combined with Fig. 9 , HAGA initializes the population with better quality under the NNC rule, which seizes a head start for the subsequent optimality search. From the iterative curve, HAGA and AGA are better than GA and HPSO, which shows that the use of adaptive genetic strategy can help to maintain the diversity of the population and prevent the algorithm from converging to the local optimal solution too early. The superior search quality of HAGA compared to AGA shows that the global destroy-repair mechanism of the LNS algorithm can enhance the algorithm's ability of local search, and to a certain extent, prevent the algorithm from falling into local optimums. In addition, in terms of solution stability, after 20 runs, the percentage deviation of HAGA in the final objective function result is only 1.4%, while for the other algorithms is more than 15%, which sufficiently demonstrates that the solution of HAGA has stronger stability.

Discussion of the results of the strategy using different distribution cycle intervals

The solution results for different cycle intervals are not directly comparable, so in this paper, the magnitude is transformed to be on the unit of the month. The model solution results using the HAGA are shown in Table 7 after the comparability transformation.

The general trend of the different cycle intervals of distribution in various indicators is that the smaller the cycle interval, the higher the carbon dioxide emissions during distribution, the higher the overall logistics cost, but the better the improvement in inventory levels. Considering the current situation of the RT logistics center, a 31% reduction in the inventory level can sufficiently alleviate the current operational pressure. Therefore, the decision was made to adopt a distribution plan with a 3-day distribution cycle interval. In the future, according to the actual situation of the logistics center flexibly change the distribution cycle interval, in order to adapt to the needs of the development promptly. The specific distribution program is shown in Table 8 .

In addition, the adoption of a circular distribution model with a 3-day interval between delivery cycles has resulted in a 66.78% reduction in overall logistics cost, an 18.08% reduction in carbon dioxide emissions, and a 31% reduction in inventory levels compared to the initial point-to-point bulk delivery model, which is a significant improvement.

Simulation analysis under medium and large scale arithmetic examples

For those enterprises with more complex supply chains, they are located in industrial clusters with a large number of suppliers, and the scale of distribution at the reverse logistics terminal will also be larger. Therefore, this paper conducts extended experiments for medium and large-scale cases to make this study more universal for industry. At the same time, the robustness of the HAGA designed in this paper is examined using different example data to deepen the significance of the study.

Description of the simulation example

The randomized generation method is used to generate the medium and large-scale example data with the number of distribution sites of 40, 50, 60, and 70, respectively. The coordinates of each distribution site are randomly generated from 0 to 50 km, the number of cargo units required is randomly generated from 1 to 8, and the weight of materials is randomly generated from 200 to 2000 kg. The rest of the parameter settings are consistent with those in the previous section.

Simulation example solving

The solution is solved for different size cases and the results are shown in Table 9 .

Table 9 shows that the HAGA continues to outperform the other algorithms at all scales. It outperforms the other algorithms in terms of the total objective function $Z$ , the number of trips, and the number of vehicles transported, which is consistent with the conclusions drawn in “ Result discussion ” section. In terms of the objective function $Z_{1}$ , the HAGA outperforms the AGA by an average of 17.35%, the GA by an average of 25.41%, and the HPSO by an average of 20.79%. In terms of the performance of the objective function $Z_{2}$ , the HAGA outperforms the AGA by 18.29% on average, the GA by 22.59% on average, and the HPSO by 21.67% on average. In addition, as the size of the arithmetic cases increases, HAGA has a greater advantage over the rest of the algorithms, proving that it possesses strong robustness. Meanwhile, in terms of convergence speed, the HAGA is able to reach the optimization with fewer iterations. However, in terms of solution time, HAGA is at a disadvantage compared to the other algorithms.

Focusing on the strategy of sustainable development, this paper studies the problem of green distribution of automobile scrap reverse logistics for industrial cluster areas. The main conclusions are as follows:

Under the comprehensive consideration of reducing the inventory level and realizing the cost reduction and efficiency improvement of logistics, a circular distribution mode based on Milk-run is proposed to replace the initial point-to-point batch distribution mode. To achieve the coordination between sustainable development strategy and enterprise cost-effectiveness, this paper introduces multiple optimization objectives of minimizing carbon dioxide emissions, overall logistics cost, and delayed delivery, constructs the MTGVRPTW model, and verifies the usability of the model.

Given the characteristics of this research problem, a hybrid adaptive genetic algorithm that combines the nearest neighbor rule based on minimum cost, adaptive strategy, bin-packing algorithm based on the transfer-of-state equation, large-scale neighborhood search algorithm and genetic algorithm, and the design process of the algorithm is described in detail. The robustness and stability of the HAGA algorithm are verified by the numerical calculation.

To efficiently obtain the location data of supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Validation results indicate that this method achieves an overall average accuracy of 98.88% in coordinate extraction, characterized by high efficiency and accuracy.

A case study was conducted on the RT logistics center to analyze its operational performance under different distribution strategies. The results indicate that shortening the distribution cycle interval significantly reduces inventory levels but also increases logistics costs and carbon dioxide emissions. The analysis concluded that adopting a three-day cycle distribution model better meets the current development needs of the RT logistics center. Compared to the initial point-to-point batch distribution model, overall logistics cost decreased by 66.78%, carbon dioxide emissions reduced by 18.08%, and inventory levels dropped by 31%, demonstrating significant improvements. In addition, by introducing medium-to-large-scale simulation examples, the significant advantages of the HAGA algorithm over other algorithms in terms of robustness and optimization ability are verified. It also shows that the research results have good application universality in similar industries.

Data availability

Some of the data used in this study are presented in the manuscript, and the remaining data are available upon reasonable request from the corresponding author. The complete data are not directly disclosed because they may compromise the privacy of the study participants.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Heilongjiang Province, Grant No. LH2023G001.

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Hongyu Wang, Huicheng Hao & Mengdi Wang

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H.W.: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Validation, Project administration, Investigation, Writing—original draft. H.H.: Funding acquisition, Supervision, Writing—review and editing. M.W.: Visualization. All authors reviewed the manuscript.

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Wang, H., Hao, H. & Wang, M. Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy. Sci Rep 14 , 17305 (2024). https://doi.org/10.1038/s41598-024-68112-4

Received : 03 January 2024

Accepted : 19 July 2024

Published : 27 July 2024

DOI : https://doi.org/10.1038/s41598-024-68112-4

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Here is a six-step process to follow when using a problem-solving model: 1. Define the problem. First, determine the problem that your team needs to solve. During this step, teams may encourage open and honest communication so everyone feels comfortable sharing their thoughts and concerns.

A TIPS process centered on data. Team Initiated Problem Solving (TIPS) Watch overview video. TIPS is a research-validated framework to use during any team meeting focused on data-driven decision making.In the TIPS model, every team needs a minute taker, a facilitator, a data analyst, and at least one additional person available to be a backup to these roles if anyone is absent.

inferences based on a model library. Independence of these two constituents of model-based systems is not to be under-stood at a low technical level (data structures), but at a conceptual level: the models should be stated in a way that is not committed to one particular task; and the problem solving engine should avoid encoding speciﬁcities ...

4. Data-Based Problem Solving: A Framework for Practice by School Psychologists. Jamilia J. Blake & Mary Barringer. SECTION 2. Domain 1: Data-Based Decision Making. 5. Best Practices in Improving Data-Based Decision Making in Schools Stacy-Ann A. January. 6. Best Practices for Universal Screening in Schools Craig A. Albers & Garret J. Hall. 7.

This book offers knowledge, strategies, and tools that will help you use data effectively. Applicable to any content area, the four simple steps in this book's problem-solving model include specific questions that will guide your use of data to identify and solve problems that stand in the way of student achievement.

The Minneapolis Public Schools (MPS) has a long history of using data-based decision-making, the problem-solving model (PSM), and response to intervention (RTI). This commitment can be traced to the special education department's adoption of the principles outlined in Deno and Mirkin's (1977) Data-Based Program Modification .

A Practice Profile is an instrument used to operationalize the features of a practice, program, and/or system. This Practice Profile defines the guiding principles and critical components of Data-Based Problem Solving and Decision-Making, an essential component of MTSS implementation. This profile defines this essential component according to ...

Data-Based Problem-Solving and Decision-Making is an Essential Element of NeMTSS because the problem-solving model is a universal process that can be used throughout education. During Systems Training, school districts learn about the method and are encouraged to use it in a situation in their school.

Training Model: The School Psychologist as a Data-based Problem-Solver ... provide students with the knowledge base and skills necessary to implement specific data-based problem-solving strategies during their field experiences and to acquire new strategies in the future. The Education dimension includes coursework and experience related to: (a ...

Abstract. Making decisions is a core activity in schools. Every school has faculty teams that meet regularly to make decisions concerning logistical, administrative, academic, and social issues. The thesis of this chapter is that team decisions will be more effective and efficient when they occur in the context of a formal problem-solving model ...

Abstract. The paper aims to introduce the conceptual framework of problem solving through values. The framework consists of problem analysis, selection of value (s) as a background for the solution, the search for alternative ways of the solution, and the rationale for the solution. This framework reveals when, how, and why is important to ...

2.1: The School Implementation Team develops and uses a problem-solving culture of continuous improvement that extends from the school to the classroom to the individual student level. Needs are assessed by system data, both aggregated and disaggregated, to determine current staff and student needs, assets, gaps, and resource allocation within the district.

The following Data-Based Problem-Solving and Decision-Making items are included on the NeMTSS Self-Assessment: Integrated data-based problem-solving for student academic, behavior and social-emotional outcomes occurs across content areas, grade levels and continuum of supports. NeMTSS leadership teams use student data and implementation data to ...

The problem-solving model (PSM) is used in the Minneapolis Public Schools to guide decisions regarding: (1) interventions in general education, (2) referral to special education, and (3 ...

Learn how to change instruction, curriculum, and environment to better support students. K-12 educators and leaders will: Understand the proper application of data-based decision making. Successfully utilize data to improve student learning. Use worksheets and tools to organize data team meetings. Become data rich and information rich.

o implement effective and efficient problem solving (Nellis, 2012). Team-Initiated Problem Solving (TIPS) is a framework that addresses these barriers by breaking down problem solving into six critical steps to guide teams through. data-based decision making process that leads to desired outcomes. TIPS also infuses critical elements of ...

Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don't know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there's a lot of divergent thinking initially.

Keywords: School teams, data-based decision-making, problem-solving, positive behavior interventions and supports There problem-solving is a long history teams of (Bahr school & personnel Kovaleski, serving 2006). ... Look at effects) to develop a data-based problem-solving model for use in schools (Problem identification-Problem definition ...

Describes a data-based problem-solver model (DBPS) for training in school psychology and recommends it as a superordinate model that can facilitate implementation of scientist-practitioner training in all school psychology programs. The fundamental principles and primary components of the DBPS are illustrated by describing its use in a doctoral school psychology training program. The DBPS ...

The MTSS problem-solving model is a data-driven decision-making process that helps educators utilize and analyze interventions based on students' needs on a continual basis. Traditionally, the MTSS problem-solving model only involves four steps: Identifying the student's strengths and needs, based on data.

Family-School Partnerships: Five Tips for Successful Problem Solving With Parents; Seven Habits of Highly Effective Schools: How School Psychologists Can Promote School Change ... Following the framework for the 2020 NASP Practice Model, Volume 1: Data-based and Collaborative Decision Making features 24 chapters focused on Domains 1 and 2.

Generally, this procedure refers to the process of continuing record-keeping activities to determine whether the problem occurs in the future. Usually, the school psychologist and consultee select. Problem-Solving Consultation Data-Based and Collaborative Decision Making, Ch. 30 475. Review Copy Not for Distribution.

The problem solving model is a ten-step model to guide you through a structured problem solving process. Focuses on data based decisions. 800 -274-2874; [email protected] ; Cart; ... The statistical tools help us make data-based decisions at various points throughout the model. The group problem-solving skills help us draw on the benefits of ...

Following the research path from GPT, GPT-2, and GPT-3, our deep learning approach leverages more data and more computation to create increasingly sophisticated and capable language models. ... We used GPT-4 to help create training data for model fine-tuning and iterate on classifiers across training, evaluations, and monitoring.

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