Learning (Re-)Starting Solutions for Vehicle Routing Problems

TL;DR

This paper introduces a machine learning approach that trains a value network to evaluate and guide solution exploration in vehicle routing problems, improving the efficiency of restarting and initializing search processes.

Contribution

It presents a novel 'Learn to Restart' algorithm that leverages a value network to enhance search strategies for Capacitated Vehicle Routing Problems.

Findings

The method accelerates solution finding in CVRPs.

It outperforms traditional restart strategies.

The approach effectively guides exploration in combinatorial optimization.

Abstract

A key challenge in solving a combinatorial optimization problem is how to guide the agent (i.e., solver) to efficiently explore the enormous search space. Conventional approaches often rely on enumeration (e.g., exhaustive, random, or tabu search) or have to restrict the exploration to rather limited regions (e.g., a single path as in iterative algorithms). In this paper, we show it is possible to use machine learning to speedup the exploration. In particular, a value network is trained to evaluate solution candidates, which provides a useful structure (i.e., an approximate value surface) over the search space; this value network is then used to screen solutions to help a black-box optimization agent to initialize or restart so as to navigate through the search space towards desirable solutions. Experiments demonstrate that the proposed ``Learn to Restart'' algorithm achieves promising results in solving Capacitated Vehicle Routing Problems (CVRPs).