ISYE 671 Linear Programming and Network Flows

Spring 2026

Course Project Requirements

1. Team composition. Each team should consist of 1 or 2 students. Each team should designate one student to be the person who is responsible for submissions on Blackboard.

2. Purpose. The course project is a comprehensive quantitative study of a decision-making problem solved by optimization methods. The purpose is to practice formulating real-world problems as optimization models, implementing them computationally, analyzing results, and advising decision-making. The project should demonstrate work that goes beyond the scope of homework and exam problems in complexity, modeling judgment, and analytical depth.

3. Case study selection. Choose one of the six case studies provided by the instructor. The case studies cover the following topic areas:

1. Linear Programming

2. Integer Programming

3. Network Models

4. Routing Problems

5. Stochastic Linear Programming

6. Large-Scale Optimization

Email the instructor with your team members and your case study choice by Monday, March 30, 11:59 pm. No more than two teams may select the same case study. Selections are first-come, first-served.

4. Scope of work. For your selected case study, you are expected to:

1. Formulate the optimization model rigorously. Define all sets, parameters, decision variables, the objective function, and all constraints. State and justify any assumptions you make.

2. Implement the model in a Google Colab notebook.

3. Solve the model and present the solution in clear, well-formatted tables and/or visualizations.

4. Analyze the results. Conduct the scenario analysis, sensitivity analysis, or other analyses specified in the case study. Interpret the results and discuss insights.

5. Advise. Write a managerial report or executive memo as specified in the case study, translating the optimization results into actionable recommendations for a decision-maker audience.

5. Methodology. You must use methods taught in this course (linear programming, integer programming, network flows, stochastic programming, or column generation, as appropriate). You may also use other tools when useful, such as Python for data processing, NetworkX for graph visualization, or matplotlib for plotting. All optimization models must be implemented in AMPL.

6. Deliverables. Each team submits the following on Blackboard:

1. Project report (PDF or Word document). Due Friday, May 8, 11:59 pm.

2. Google Colab notebook (.ipynb file). The notebook must run without errors and reproduce all results reported in the written report.

3. Presentation slides (PDF or PowerPoint). Used for the in-class presentation.

7. In-class presentation. Each team will give a 12–15 minute presentation during class on Monday, April 27. The presentation should cover the problem context and why it matters, the model formulation (key variables and constraints), the solution and main results, and your managerial recommendations. All team members must participate in the presentation.

8. Components of the project report:

1. Introduction. An overview of the case study. What is the problem? What decisions need to be made? What makes this problem challenging?

2. Model formulation. The complete mathematical formulation: sets, parameters, decision variables, objective function, and all constraints. Include a discussion of your modeling assumptions and choices.

3. Implementation. Describe how you implemented the model in Python/AMPL. Discuss any implementation challenges, solver settings, or data processing steps. Reference the Colab notebook.

4. Results. Present the optimal solution with clear tables and/or figures. Report solver status, objective value, and key decision variable values.

5. Analysis. Scenario analysis, sensitivity analysis, or other analyses as specified in the case study. Interpret the results: what do they tell the decision-maker?

6. Managerial report/memo. The executive summary or recommendation memo specified in the case study, written for a non-technical audience.

7. Conclusion. Summarize key findings. Discuss limitations of the model and potential extensions.

8. References. Cite any sources used, including textbook chapters, online resources, and AI tools.

9. Team contribution statement. Each team member’s specific responsibilities and tasks in the project.

10. AI usage statement. How AI is used and links to chat histories.

9. Format of the project report:

1. Margins: 1 inch on all four sides.

2. Font: Times New Roman.

3. Font size: 12 points. Use appropriate font sizes in equations, tables, and figures for readability.

4. Line spacing: single (1.0).

5. Include a cover page with the title of the case study, team member names, and the date.

6. Page limit: 15 pages. Cover page, references, appendix, and the managerial memo do not count toward the page limit.

10. Use of AI tools. Consistent with the course AI policy (see syllabus), you are encouraged to use AI tools (e.g., ChatGPT, Claude, Gemini, GitHub Copilot) as technical co-pilots for coding, debugging, and drafting. However:

1. You are fully responsible for the correctness of all work submitted. AI-generated code and text must be verified and validated.

2. You must disclose all AI usage and describe how each tool was used (e.g., model drafting, code debugging, writing assistance).

3. For chat-based AI tools, provide a shareable link to the complete chat history, referenced in your report.

4. You must be prepared to explain and defend all aspects of your work, including any AI-generated content, during the presentation and if asked by the instructor.