ADVANCED OPTIMIZATION MODELING
Translation of problems into optimization models, including how to implement these models in relevant software. Specific topics include techniques to create models of problems with discrete decisions that can be efficiently solved by available software and modeling approaches for problems impacted by uncertainty.
Topics
- techniques to create models of problems with discrete decisions that can be efficiently solved by available software and modeling approaches for problems impacted by uncertainty
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: I SY E 323 or E C E/COMP SCI/I SY E 524 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Learning Outcomes: 1. Create optimization models for a wide range of realistic problem settings Audience: Both Grad & Undergrad 2. Write models that can be solved more quickly by available solvers Audience: Both Grad & Undergrad 3. Implement these models using available software Audience: Both Grad & Undergrad 4. Provide theoretical explanations for why modeling a problem in a particular way may make it easier to solve using available methods Audience: Graduate Learning Outcomes: 1. Create optimization models for a wide range of realistic problem settings Audience: Both Grad & Undergrad 2. Write models that can be solved more quickly by available solvers Audience: Both Grad & Undergrad 3. Implement these models using available software Audience: Both Grad & Undergrad 4. Provide theoretical explanations for why modeling a problem in a particular way may make it easier to solve using available methods Audience: Graduate
Sources
- Course catalogue ↗2026-2027