The Big M method is a widely-used technique in mixed integer linear programming (MILP) to incorporate logical decision-making. While it is a powerful tool for modeling diverse real-world decision problems, it has a significant drawback: the potential to cause numerical instability in MILP solvers. This instability can lead to a solver terminating with a sub-optimal solution or incorrectly reporting the problem as infeasible.

In this tutorial, participants will observe the side effects of using the Big M method when solving MILP problems with open-source solvers like HiGHS, SCIP, and CBC. To facilitate this exercise, participants will have the opportunity to formulate and solve MILP examples using the Elytica Interpreter. Since this is an interactive tutorial, delegates are encouraged to bring their own laptops. The Elytica Interpreter may be accessed through a web browser.

Prof Fanie Terblanche is an optimization specialist with over 20 years of experience in applying algorithmic approaches to solving large-scale optimization problems. His work involves the application of exact and decomposition methods to improve computing times and solution quality. As an extraordinary professor in the Faculty of Engineering, North-West University, he is actively publishing in peer-reviewed journals and is a supervisor to several master’s and PhD students. Fanie is the founder and managing director of Elytica. He is currently President of ORSSA.

This tutorial takes place during the formal conference programme and is included for free as part of the delegate's registration ticket.