Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows

TL;DR

This paper presents a reinforcement learning method using attention-based neural networks to optimize stochastic vehicle routing with time windows, outperforming traditional heuristics in reducing travel costs.

Contribution

It introduces a novel reinforcement learning framework for SVRP that accounts for uncertainty and integrates external data, advancing beyond heuristic-based solutions.

Findings

Achieved 1.73% reduction in travel costs over Ant-Colony Optimization.

Demonstrated robustness across diverse environments.

Provides a new benchmark for future SVRP research.

Abstract

This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery. We develop a novel SVRP formulation that accounts for uncertain travel costs and demands, alongside specific customer time windows. An attention-based neural network trained through reinforcement learning is employed to minimize routing costs. Our approach addresses a gap in SVRP research, which traditionally relies on heuristic methods, by leveraging machine learning. The model outperforms the Ant-Colony Optimization algorithm, achieving a 1.73% reduction in travel costs. It uniquely integrates external information, demonstrating robustness in diverse environments, making it a valuable benchmark for future SVRP studies and industry application.