Process optimization in industry has become essential in order to maximize the resources available and reduce energy consumption.
Optimization problems become interesting when dealing with restrictions (linear or nonlinear) and integer variables (modeling the discrete decisions). Python ecosystem presents different libraries to solve optimization problems, some of them are CVXOpt, CVXPy, PulP, OpenOpt, or Pyomo. Among them, Pyomo results interesting because:
The talk will be divided in three parts:
Introduction to Mathematical Programming/Optimization (15 min): visual introduction to optimization concepts including restrictions and non linearties (linear Programming, Nonlinear Programming, ILP, MIP, MINLP).
Introduction to the Pyomo sintax and a quick note for the installation (20 min): showing how to improve their diet and save money when ordering food in fast food restaurants.
Optimization problems in engineering (10 min): showing more advanced optimization examples that include decision variables.