Mathematical Optimization with GAMS and Pyomo (Python)

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What you’ll learn:

• Mathematical optimization
• Linear programming
• Integer programming
• Nonlinear programming
• Hands-on coding experience in GAMS
• Hands-on coding experience in Pyomo (Python)

Description:

This introductory course to optimization in GAMS and Pyomo (Python) contains 4 modules, namely,

• Linear programming
• Nonlinear programming
• Mixed Integer Linear Programming, and
• Mixed-Integer Nonlinear Programming

In each module, we aim to teach you the basics of each type of optimization through 3 different illustrative examples and 1 assingment from different areas of science, engineering, and management. Using these examples, we aim to gently introduce you to coding in two environments commonly used for optimization, GAMS and Pyomo. GAMS is a licensed software, for which we use a demo license in this course. Pyomo is an open-source package in Python, which we use Google Colaboratory to run. As we proceed through the different examples in each module, we also introduce different functionalities in GAMS and Python, including data import and export.

At the end of this course, you will be able to,

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1. Read a problem statement and build an optimization model
   1. Be able to identify the objective function, decision variables, constraints, and parameters
2. Code an optimization model in GAMS
   1. Define sets, variables, parameters, scalars, equations
   2. Use different solvers in GAMS
   3. Leverage the NEOS server for optimization
   4. Import data from text, gdx, and spreadsheet files
   5. Export data to text, gdx, and spreadsheet files
   6. Impose different variable ranges, and bounds
3. Code an optimization model in Pyomo
   1. Define models, sets, variables, parameters, constraints, and objective function
   2. Use different solvers in Pyomo
   3. Leverage the NEOS server for optimization
   4. Import data from text, gdx, and spreadsheet files
   5. Export data to text, gdx, and spreadsheet files
   6. Impose different variable ranges, and bounds

Who this course is for:

• We have designed this course to be accessible to students and professionals in various disciplines including, but not limited to, operations research, engineering, science, and management. Hence, we have chosen illustrative examples for each module within the course from different disciplines such as production scheduling, chemical and electrical engineering, geometry, etc. For each example, we will go through the problem statement in detail before proceeding to coding in GAMS and Python, so rest assured that you can follow through regardless of your field of learning and work.

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