Abstract
Optimization with constraint learning (OCL) uniquely leverages machine learning (ML) to design optimization models in which constraints and objectives are directly learned from data whenever explicit expressions are unknown. While OCL offers great advantages to design more accurate models, in a faster way, practitioners should also be aware of possible pitfalls and inaccuracies arising from embedding fitted models as optimization constraints. Divided into four parts, the OCL Lab offers theoretical as well as hands-on tutorials, demonstrated on a case study from the World Food Programme. Throughout the OCL Lab, participants will become familiar with two novel Python packages: (1) OptiCL to learn and embed constraints and (2) DOFramework to evaluate the optimal solutions generated by an OCL algorithm. The first two parts of the lab will provide participants with theoretical and practical knowledge for using ML models to learn constraints and objectives directly from data. The remaining two parts will be dedicated to novel quality metrics for OCL and a structured testing framework for OCL algorithms.