Liner Shipping Network Design with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach for designing cost-efficient maritime shipping networks, outperforming traditional heuristics and demonstrating strong generalization on benchmark instances.

Contribution

It presents a novel RL framework for LSNDP that integrates with a heuristic flow solver, offering a new approach to this complex combinatorial problem.

Findings

Competitive results on LINERLIB benchmark

Effective generalization to perturbed instances

Outperforms traditional heuristic methods

Abstract

This paper proposes a novel reinforcement learning framework to address the Liner Shipping Network Design Problem (LSNDP), a challenging combinatorial optimization problem focused on designing cost-efficient maritime shipping routes. Traditional methods for solving the LSNDP typically involve decomposing the problem into sub-problems, such as network design and multi-commodity flow, which are then tackled using approximate heuristics or large neighborhood search (LNS) techniques. In contrast, our approach employs a model-free reinforcement learning algorithm on the network design, integrated with a heuristic-based multi-commodity flow solver, to produce competitive results on the publicly available LINERLIB benchmark. Additionally, our method also demonstrates generalization capabilities by producing competitive solutions on the benchmark instances after training on perturbed instances.