Liner Shipping Network Design with Reinforcement Learning

• URL: http://arxiv.org/abs/2411.09068v1
• Date: Wed, 13 Nov 2024 22:49:16 GMT
• Title: Liner Shipping Network Design with Reinforcement Learning
• Authors: Utsav Dutta, Yifan Lin, Zhaoyang Larry Jin,
• Abstract summary: This paper proposes a novel reinforcement learning framework to address the Liner Shipping Network Design Problem (LSNDP) Our approach employs a model-free reinforcement learning algorithm on the network design, integrated with aLIB-based multi-commodity flow solver.
• Score: 1.833650794546064
• License:
• 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.

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