Reinforcement Learning for Solving Stochastic Vehicle Routing Problem

TL;DR

This paper introduces a novel reinforcement learning framework for the stochastic vehicle routing problem, achieving cost reductions and demonstrating robustness across various scenarios, advancing RL applications in logistics optimization.

Contribution

The paper presents an end-to-end RL-based approach specifically designed for SVRP, outperforming existing metaheuristics and providing a publicly available implementation.

Findings

Achieved 3.43% reduction in travel costs.

Demonstrated robustness across diverse SVRP scenarios.

Provided a publicly available RL framework for SVRP.

Abstract

This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.