This workshop introduces participants to using CPLEX in Python through the docplex modeling library and the CPLEX Python API to develop and solve optimization models. Participants are expected to have prior experience with Python and a basic understanding of mathematical optimization, but no familiarity with CPLEX is required.

We will cover the core elements of model construction, including defining decision variables, formulating linear constraints, specifying objectives, and extracting solutions. The workshop will also present practical solver control and troubleshooting techniques, such as adjusting common parameter settings, interpreting the CPLEX log, and evaluating solution quality. In addition, we will explore a selection of advanced capabilities—including callbacks, lazy constraints, solution pools, and other specialized features of the CPLEX solver—as time permits.

Throughout the workshop, we will work through concrete examples from optimization to illustrate practical modeling workflows and to demonstrate how to effectively use CPLEX in Python.

In English only