Supervised Permutation Invariant Networks for solving the CVRP with bounded fleet size

Daniela Thyssens, Jonas Falkner, Lars Schmidt-Thieme

Keywords: Deep Learning, Combinatorial Optimization, Vehicle Routing

Abstract:

Learning to solve combinatorial optimization problems, such as the vehicle routing problem, offers great computational advantages over classical operation research solvers and heuristics. The recently developed deep reinforcement learning approaches either improve an initially given solution iteratively or sequentially construct a set of individual tours.
However, all existing learning-based approaches are not able to work for a fixed number of vehicles and thus bypass the NP-hardness of the original problem. On the other hand, this makes them less suitable for real applications, as many logistic service providers rely on solutions provided for a specific bounded fleet size and cannot accommodate short term changes to the number of vehicles.
In contrast we propose a powerful supervised deep learning framework that constructs a complete tour plan from scratch while respecting an apriori fixed number of vehicles.
In combination with an efficient post-processing scheme, our supervised approach is not only much faster and easier to train but also achieves competitive results that incorporate the practical aspect of vehicle costs.
In thorough controlled experiments we re-evaluate and compare our method to multiple state-of-the-art approaches where we demonstrate stable performance and shed some light on existent inconsistencies in the experimentation protocols of the related work.

One-sentence Summary: The paper presents an end-to-end supervised deep learning framework which is able to solve capacitated vehicle routing problems with an apriori fixed fleet size, which is benchmarked against leading reinforcement learning based approaches.