Multi-objective Adaptive Dynamics Attention Model to Solve Multi-objective Vehicle Routing Problem

Guang Luo, Jianping Luo
Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:834-849, 2024.

Abstract

Multi-objective combinatorial optimization problems (MOCOP) are commonly encountered in everyday life. However, finding the optimal solution through traditional exact and heuristic algorithms can be time-consuming due to its NP-hard nature. Fortunately, deep reinforcement learning (DRL) has shown promise in solving complex combinatorial optimization problems (COP). In this paper, we introduce a new Multi-objective Adaptive Dynamics Attention Model (MOADAM) that aims to better approximate the whole Pareto set. We modify the encoder and decoder of the model to better utilize dynamic information, and we also design a new weight sampling method to improve the model’s performance for extreme solutions. Our experimental results demonstrate that our proposed model outperforms the current state-of-the-art algorithm in terms of solution quality on multi-objective vehicle routing problems with capacity constraints (MOCVRP).

Cite this Paper

BibTeX
@InProceedings{pmlr-v222-luo24a, title = {Multi-objective Adaptive Dynamics Attention Model to Solve Multi-objective Vehicle Routing Problem}, author = {Luo, Guang and Luo, Jianping}, booktitle = {Proceedings of the 15th Asian Conference on Machine Learning}, pages = {834--849}, year = {2024}, editor = {Yanıkoğlu, Berrin and Buntine, Wray}, volume = {222}, series = {Proceedings of Machine Learning Research}, month = {11--14 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v222/luo24a/luo24a.pdf}, url = {https://proceedings.mlr.press/v222/luo24a.html}, abstract = {Multi-objective combinatorial optimization problems (MOCOP) are commonly encountered in everyday life. However, finding the optimal solution through traditional exact and heuristic algorithms can be time-consuming due to its NP-hard nature. Fortunately, deep reinforcement learning (DRL) has shown promise in solving complex combinatorial optimization problems (COP). In this paper, we introduce a new Multi-objective Adaptive Dynamics Attention Model (MOADAM) that aims to better approximate the whole Pareto set. We modify the encoder and decoder of the model to better utilize dynamic information, and we also design a new weight sampling method to improve the model’s performance for extreme solutions. Our experimental results demonstrate that our proposed model outperforms the current state-of-the-art algorithm in terms of solution quality on multi-objective vehicle routing problems with capacity constraints (MOCVRP).} }
Endnote
%0 Conference Paper %T Multi-objective Adaptive Dynamics Attention Model to Solve Multi-objective Vehicle Routing Problem %A Guang Luo %A Jianping Luo %B Proceedings of the 15th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Berrin Yanıkoğlu %E Wray Buntine %F pmlr-v222-luo24a %I PMLR %P 834--849 %U https://proceedings.mlr.press/v222/luo24a.html %V 222 %X Multi-objective combinatorial optimization problems (MOCOP) are commonly encountered in everyday life. However, finding the optimal solution through traditional exact and heuristic algorithms can be time-consuming due to its NP-hard nature. Fortunately, deep reinforcement learning (DRL) has shown promise in solving complex combinatorial optimization problems (COP). In this paper, we introduce a new Multi-objective Adaptive Dynamics Attention Model (MOADAM) that aims to better approximate the whole Pareto set. We modify the encoder and decoder of the model to better utilize dynamic information, and we also design a new weight sampling method to improve the model’s performance for extreme solutions. Our experimental results demonstrate that our proposed model outperforms the current state-of-the-art algorithm in terms of solution quality on multi-objective vehicle routing problems with capacity constraints (MOCVRP).
APA
Luo, G. & Luo, J.. (2024). Multi-objective Adaptive Dynamics Attention Model to Solve Multi-objective Vehicle Routing Problem. Proceedings of the 15th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 222:834-849 Available from https://proceedings.mlr.press/v222/luo24a.html.

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