A MAB-based hyper-heuristic for vehicle routing problem with time window

Yan-e Hou; Bingbing Liu; Gaojuan Fan

doi:10.1117/12.2674549

28 April 2023 A MAB-based hyper-heuristic for vehicle routing problem with time window

Yan-e Hou, Bingbing Liu, Gaojuan Fan

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126260A (2023) https://doi.org/10.1117/12.2674549
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

In order to efficiently solve the vehicle routing problem with time window (VRPTW), a hyper-heuristic algorithm based on reinforcement learning was proposed. Firstly, the performance of the underlying heuristic algorithm was evaluated, and then a multi-armed bandit (MAB) algorithm was used to select the low-level heuristic algorithm. At the same time, a simulated annealing-based acceptance rule was used to determine whether to accept the solution obtained by each lowlevel heuristic to ensure the diversity of solutions. Experiments was carried out on some VRPTW benchmark instances, and the results show that the proposed algorithm is effective and stable.

Citation Download Citation

Yan-e Hou, Bingbing Liu, and Gaojuan Fan "A MAB-based hyper-heuristic for vehicle routing problem with time window", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126260A (28 April 2023); https://doi.org/10.1117/12.2674549

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