Mathematical Optimization with GAMS and Pyomo (Python)

About Mathematical Optimization with GAMS and Pyomo (Python)

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,

Read a problem statement and build an optimization model

Be able to identify the objective function, decision variables, constraints, and parameters

Code an optimization model in GAMS

Define sets, variables, parameters, scalars, equations

Use different solvers in GAMS

Leverage the NEOS server for optimization

Import data from text, gdx, and spreadsheet files

Export data to text, gdx, and spreadsheet files

Impose different variable ranges, and bounds

Code an optimization model in Pyomo

Define models, sets, variables, parameters, constraints, and objective function

Use different solvers in Pyomo

Leverage the NEOS server for optimization

Import data from text, gdx, and spreadsheet files

Export data to text, gdx, and spreadsheet files

Impose different variable ranges, and bounds