Fifteen weeks, weighted toward the part that is actually hard. Weeks 1–5, model construction: turning a plant problem into algebra that says what you meant. Week 6: drafting models with AI assistance, and proving them right or wrong. Weeks 7–13, theory and methods: what solvers do, why they behave as they do, and where data-driven methods beat mathematical programming.
These open in the browser — no download, and they work on a phone. Press F for full screen, E for a printable layout, ? for every shortcut.
| # | Session | Date | Hands-on |
|---|---|---|---|
| 1 | Introduction, the optimization workflow, and the tool stack Orientation |
10 Aug | Excel Solver |
| 2 | Modeling I: linear models, product mix, multiperiod planning and inventory Model construction |
17 Aug | OpenSolver |
| 3 | Modeling II: networks, flows and process superstructures Model construction |
24 Aug | OpenSolver |
| 4 | Modeling III: blending, pooling and quality specifications Model construction |
31 Aug | Excel, then Python |
| 5 | Modeling IV: logical and discrete decisions Model construction |
7 Sep | OpenSolver and Pyomo |
| 6 | AI-assisted modeling and model debugging Tools and verification |
14 Sep | Pyomo · workshop |
| 7 | Linear programming theory and solver behaviour Theory and algorithms |
21 Sep | Python |
| 8 | Duality and sensitivity analysis Theory and algorithms |
28 Sep | Excel and Pyomo |
| 9 | Unconstrained nonlinear programming Theory and algorithms |
5 Oct | Python |
| 10 | Constrained nonlinear programming Theory and algorithms |
12 Oct | Pyomo and Ipopt |
| 11 | Integer and mixed-integer programming Theory and algorithms |
19 Oct | Pyomo and CBC |
| 12 | Optimization under uncertainty Advanced topics |
26 Oct | Pyomo |
| 13 | Data-driven and AI-based optimization Advanced topics |
2 Nov | Python |
| 14 | Project presentations, part 1 Synthesis |
9 Nov | — |
| 15 | Project presentations, part 2, and wrap-up Synthesis |
16 Nov | — |
| Weight | Component | What it asks |
|---|---|---|
| 35% | Final examination | Cumulative, weighted to Weeks 9 to 13 — nonlinear and integer programming, uncertainty, data-driven methods |
| 25% | Midterm examination | Weeks 1 to 8, weighted to model construction. Includes a model-verification question. No nonlinear programming |
| 15% | Assignments | Four problem sets — model construction, LP and duality, nonlinear programming, integer programming |
| 15% | Computational project | A real optimization problem of your own, modelled in Pyomo, with a test suite and a presentation |
| 10% | Participation and quizzes | In-class activities, the weekly notebook, short quizzes |