A deep reinforcement learning framework with generalization performance for the large-scale capacitated vehicle routing problem

Xiaoxiao Yang; Lin Ke; Zhibin Chen

doi:10.1117/12.2672270

28 March 2023 A deep reinforcement learning framework with generalization performance for the large-scale capacitated vehicle routing problem

Xiaoxiao Yang, Lin Ke, Zhibin Chen

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Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 1259736 (2023) https://doi.org/10.1117/12.2672270
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

Combinatorial optimization has found its way into a variety of domains, including artificial intelligence and cybernetics. Deep Reinforcement Learning (DRL) has recently demonstrated its promise for developing heuristics for NP-hard routing problems. The current generalization performance of models needs to be improved, especially for large-scale routing problems. In this paper, we propose a hybrid approach for the Capacitated Vehicle Routing Problem (CVRP) based on DRL and adaptive large neighborhood search. The information representation of the neural network for CVRP is also improved by the combination of multi-head attention mechanism, pointer network and graph neural networks. The experimental results demonstrate that the optimization of our model on CVRP outperforms existing DRL techniques and some traditional algorithms. In addition, our method improves the training efficiency of the model and the performance of generalization to large-scale CVRP.

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Xiaoxiao Yang, Lin Ke, and Zhibin Chen "A deep reinforcement learning framework with generalization performance for the large-scale capacitated vehicle routing problem", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 1259736 (28 March 2023); https://doi.org/10.1117/12.2672270

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KEYWORDS

Education and training

Mathematical optimization

Performance modeling

Neural networks

Design and modelling

Computer programming

Evolutionary algorithms

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