Axel Parmentier, Ecole des Ponts

Abstract

Recent contributions have shown that embedding a combinatorial optimization layer in machine learning pipeline could provide state-of-the-art algorithms for static and dynamic vehicle routing problems. In this talk, we draw lessons from several static and dynamic application to identify classes of vehicle routing problems that can benefit from such approaches. We also detail several pipeline architectures that lead to efficient algorithms. Several approaches to learn these pipelines will also be detailed. We finally illustrate how our recent open source library InferOpt.jl simplifies the implementation and training of such pipelines.

[Video]