Learning to Approximate Industrial Problems by Operations Research Classic Problems

Abstract

Operations research (OR) practitioners are accustomed to dealing with variants of classic OR problems. Indeed, an industrial problem often looks like a traveling salesman problem, a vehicle routing problem, a shortest path problem, etc., but has an additional constraint or a different objective that prevent the use of the powerful algorithms produced by decades of research on the classic OR problems. This situation can be frustrating, notably when we realize that the classic problem catches most of the structure of the variant. In “Learning to approximate industrial problems by operations research classic problems,” Axel Parmentier introduces a machine learning approach to use the algorithms for the classic OR problems on the variant. The idea is to leverage structured learning to obtain a mapping that approximates an instance of the variant by an instance of the classic problem.