Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size

TL;DR

This paper introduces a supervised deep learning method for solving the Capacitated Vehicle Routing Problem with a fixed fleet size, offering a practical and efficient alternative to existing approaches that often lack this constraint.

Contribution

The authors develop a novel supervised neural network framework that constructs complete routing solutions respecting a fixed number of vehicles, improving speed and practicality over prior methods.

Findings

Achieves competitive routing solutions with fewer vehicles.

Faster training and inference compared to state-of-the-art methods.

Demonstrates stable performance across various experiments.

Abstract

Learning to solve combinatorial optimization problems, such as the vehicle routing problem, offers great computational advantages over classical operations 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, most of the existing learning-based approaches are not able to work for a fixed number of vehicles and thus bypass the complex assignment problem of the customers onto an apriori given number of available vehicles. 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 available 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 compare our method to multiple state-of-the-art approaches where we demonstrate stable performance, while utilizing less vehicles and shed some light on existent inconsistencies in the experimentation protocols of the related work.