Reinforcement Learning for Solving Stochastic Vehicle Routing Problem

Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takáč
Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:502-517, 2024.

Abstract

This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.

Cite this Paper

BibTeX
@InProceedings{pmlr-v222-iklassov24a, title = {Reinforcement Learning for Solving Stochastic Vehicle Routing Problem}, author = {Iklassov, Zangir and Sobirov, Ikboljon and Solozabal, Ruben and Tak\'{a}\v{c}, Martin}, booktitle = {Proceedings of the 15th Asian Conference on Machine Learning}, pages = {502--517}, 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/iklassov24a/iklassov24a.pdf}, url = {https://proceedings.mlr.press/v222/iklassov24a.html}, abstract = {This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.} }
Endnote
%0 Conference Paper %T Reinforcement Learning for Solving Stochastic Vehicle Routing Problem %A Zangir Iklassov %A Ikboljon Sobirov %A Ruben Solozabal %A Martin Takáč %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-iklassov24a %I PMLR %P 502--517 %U https://proceedings.mlr.press/v222/iklassov24a.html %V 222 %X This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.
APA
Iklassov, Z., Sobirov, I., Solozabal, R. & Takáč, M.. (2024). Reinforcement Learning for Solving Stochastic Vehicle Routing Problem. Proceedings of the 15th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 222:502-517 Available from https://proceedings.mlr.press/v222/iklassov24a.html.

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