ISYE 671Linear Optimization & Network Flows

Graduate course · Northern Illinois University · Spring 2026

Linear Optimization and Network Flows

Open materials from ISYE 671: formulating linear, integer, network, stochastic and large-scale optimization models, and solving them with AMPL and Python in Google Colab.

Dr. Ziteng Wang · Industrial and Systems Engineering

Browse materials Read the syllabus

Materials15 lecture notes and slide decks, 9 Colab labsHomework5 assignments from Spring 2026ProjectRequirements and 6 case studiesSyllabusObjectives, grading, schedule, AI policy

Topics

  1. 01Linear optimization modelingWeeks 3–4
  2. 02Integer optimizationWeeks 5–6
  3. 03Network flow modelsWeeks 7–8
  4. 04Advanced LP and IP modelsWeeks 11–12
  5. 05Optimization under uncertaintyWeek 13
  6. 06Large-scale methodsWeek 14
  7. 07Advanced topics and next stepsWeek 15

Running the labs

Labs open directly in Google Colab, so nothing needs installing. They use AMPL, which needs a free license: request one at ampl.com/ce and paste the UUID where a notebook says YOUR-AMPL-LICENSE-UUID.

Textbook

R. L. Rardin, Optimization in Operations Research, 2nd ed., Pearson. Prerequisites: an undergraduate operations research course and basic programming.

Download all files as a zip or browse them on GitHub. Found an error? Open an issue. Homework solutions and exams are not published.