Andrea Lodi, Cornell Tech

Abstract

The last decade has witnessed the impressive development of machine learning (ML) techniques. These techniques have been successfully applied to traditional statistical learning tasks as image recognition and led to breakthroughs like the famous AlphaGo system. Motivated by those successes, many scientific disciplines have started to investigate the potential of the use of large amount of data crunched by ML techniques in their context. Combinatorial optimization (CO) has been no exception to this trend and the ML use in CO has been analyzed from many different angles with various levels of success. In this talk, we will review the state of the art of such scientific path, interpreting the level of maturity reached by the integration of ML techniques in CO and discussing the challenges. We will finish by presenting one particular area in which we consider this integration having a remarkable potential, i.e., repeatedly solving CO problems with little data variations with special attention to Capacitated Vehicle Routing problems.

[Video]