A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems

Autores:: Maca-Chagüendo, Mauricio
Cobos-Lozada, Carlos-Alberto

Tipo de recurso:: Article of journal

Fecha de publicación:: 2024

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.en-US.fl_str_mv	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems
title	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems
spellingShingle	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems Capacitated Vehicle Routing Problem CVRP Metaheuristics Machine learning Artificial neural networks Reinforcement learning Problema de Enrutamiento de Vehículos con Capacidad Metaheurísticas Aprendizaje de máquina Redes neuronales artificiales Aprendizaje por refuerzo
title_short	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems
title_full	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems
title_fullStr	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems
title_full_unstemmed	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems
title_sort	A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems
dc.creator.fl_str_mv	Maca-Chagüendo, Mauricio Cobos-Lozada, Carlos-Alberto
dc.contributor.author.eng.fl_str_mv	Maca-Chagüendo, Mauricio Cobos-Lozada, Carlos-Alberto
dc.subject.none.fl_str_mv	Capacitated Vehicle Routing Problem CVRP Metaheuristics Machine learning Artificial neural networks Reinforcement learning Problema de Enrutamiento de Vehículos con Capacidad Metaheurísticas Aprendizaje de máquina Redes neuronales artificiales Aprendizaje por refuerzo
topic	Capacitated Vehicle Routing Problem CVRP Metaheuristics Machine learning Artificial neural networks Reinforcement learning Problema de Enrutamiento de Vehículos con Capacidad Metaheurísticas Aprendizaje de máquina Redes neuronales artificiales Aprendizaje por refuerzo
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-06-02 21:19:09 2026-04-16T17:47:43Z
dc.date.available.none.fl_str_mv	2024-06-02 21:19:09
dc.date.issued.none.fl_str_mv	2024-06-02
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.none.fl_str_mv	10.19053/uptc.01211129.v33.n68.2024.18379
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dc.relation.references.none.fl_str_mv	A. M. Altabeeb, A. M. Mohsen, A. Ghallab, “An improved hybrid firefly algorithm for capacitated vehicle routing problem,” Applied Software Computing, vol. 84, e105728, Nov. 2019. https://doi.org/10.1016/j.asoc.2019.105728 A. M. Altabeeb, A. M. Mohsen, L. Abualigah, A. Ghallab, “Solving capacitated vehicle routing problem using cooperative firefly algorithm,” Applied Software Computing, vol. 108, e107403, Sep. 2021. https://doi.org/10.1016/j.asoc.2021.107403 J. Cai, P. Wang, S. Sun, H. Dong, “A dynamic space reduction ant colony optimization for capacitated vehicle routing problem,” Software computing, vol. 26, no. 17, pp. 8745-8756, Sep. 2022. https://doi.org/10.1007/s00500-022-07198-2 . A. Thammano, P. Rungwachira, “Hybrid modified ant system with sweep algorithm and path relinking for the capacitated vehicle routing problem,” Heliyon, vol. 7, no. 9, e8029, Sep. 2021. https://doi.org/10.1016/j.heliyon.2021.e08029 Y. Zhou, W. Li, X. Wang, Y. Qiu, W. Shen, “Adaptive gradient descent enabled ant colony optimization for routing problems,” Swarm and Evolutionary Computation, vol. 70, e101046, Apr. 2022. https://doi.org/10.1016/j.swevo.2022.101046 P. Kalatzantonakis, A. Sifaleras, N. Samaras, “A reinforcement learning-Variable neighborhood search method for the capacitated Vehicle Routing Problem,” Expert Systems with Applications, vol. 213, e118812, Mar. 2023. https://doi.org/10.1016/j.eswa.2022.118812 A. Hottung, K. Tierney, “Neural large neighborhood search for routing problems,” Artificial Intelligence, vol. 313, e103786, Dec. 2022. https://doi.org/10.1016/j.artint.2022.103786 B. Kitchenham, P. Brereton, “A systematic review of systematic review process research in software engineering,” Information and Software Technology, vol. 55, no. 12, pp. 2049–2075, 2013. https://doi.org/10.1016/j.infsof.2013.07.010 A. García-Holgado, S. Marcos-Pablos, F. J. García-Peñalvo, “Guidelines for performing systematic research projects reviews,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 05, e05, 2020. https://doi.org/10.9781/ijimai.2020.05.005 W. Yang, L. Ke, “An improved fireworks algorithm for the capacitated vehicle routing problem,” Frontiers in Computer Science, vol. 13, no. 3, pp. 552-564, Jun. 2019. https://doi.org/10.1007/s11704-017-6418-9 Y. Wu, F. Pan, S. Li, Z. Chen, M. Dong, “Peer-induced fairness capacitated vehicle routing scheduling using a hybrid optimization ACO–VNS algorithm,” Software computing, vol. 24, no. 3, pp. 2201-2213, Feb. 2020. https://doi.org/10.1007/s00500-019-04053-9 Y. H. Jia, Y. Mei, M. Zhang, “A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem,” IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 10855-10868, Oct. 2022. https://doi.org/10.1109/TCYB.2021.3069942 N. Frias, F. Johnson, C. Valle, “Hybrid Algorithms for Energy Minimizing Vehicle Routing Problem: Integrating Clusterization and Ant Colony Optimization,” IEEE Access, vol. 11, pp. 125800-125821, 2023. https://doi.org/10.1109/ACCESS.2023.3325787 N. Sultana, J. Chan, T. Sarwar, A. K. Qin, “Learning to Optimise Routing Problems using Policy Optimisation,” in Proceedings of the International Joint Conference on Neural Networks, 2021. https://doi,org/10.1109/IJCNN52387.2021.9534010 J. Li et al., “Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem,” IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 13572-13585, Dec. 2022. https://doi.org/10.1109/TCYB.2021.3111082 Y. Wu, W. Song, Z. Cao, J. Zhang, A. Lim, “Learning Improvement Heuristics for Solving Routing Problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 5057-5069, Sep. 2022. https://doi.org/10.1109/TNNLS.2021.3068828 Y. Xu, M. Fang, L. Chen, G. Xu, Y. Du, C. Zhang, “Reinforcement Learning with Multiple Relational Attention for Solving Vehicle Routing Problems,” IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 11107-11120, Oct. 2022. https://doi.org/10.1109/TCYB.2021.3089179 K. Lei, P. Guo, Y. Wang, X. Wu, W. Zhao, “Solve routing problems with a residual edge-graph attention neural network,” Neurocomputing, vol. 508, pp. 79-98, Oct. 2022. https://doi.org/10.1016/j.neucom.2022.08.005 Y. Hou, W. Gu, C. Wang, K. Yang, Y. Wang, “A Selection Hyper-heuristic based on Q-learning for School Bus Routing Problem,” International Journal of Applied Mathematics, vol. 52, no. 4, pp. 817-825, Dec. 2022. W. Yi, R. Qu, “Automated design of search algorithms based on reinforcement learning,” Information Sciences (NY), vol. 649, e119639, Nov. 2023. https://doi.org/10.1016/j.ins.2023.119639 H. Liu, Z. Zong, Y. Li, D. Jin, “NeuroCrossover: An intelligent genetic locus selection scheme for genetic algorithm using reinforcement learning,” Applied Software Computing, vol. 146, e110680, Oct. 2023. https://doi.org/10.1016/j.asoc.2023.110680 A. Bogyrbayeva, T. Yoon, H. Ko, S. Lim, H. Yun, C. Kwon, “A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone,” Transportation Research Part C: Emerging Technologies, vol. 148, e103981, Mar. 2023. https://doi.org/10.1016/j.trc.2022.103981 W. Yi, R. Qu, L. Jiao, “Automated algorithm design using proximal policy optimisation with identified features,” Expert Systems with Applications, vol. 216, e119461, Apr. 2023. https://doi.org/10.1016/j.eswa.2022.119461 W. Yi, R. Qu, L. Jiao, B. Niu, “Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, pp. 1072-1084, Aug. 2023. https://doi.org/10.1109/TEVC.2022.3197298 D. H. Lee, J. Ahn, “Multi-start team orienteering problem for UAS mission re-planning with data-efficient deep reinforcement learning,” Applied Intelligence, vol. 54, no. 6, pp. 4467-4489, Mar. 2024. https://doi.org/10.1007/s10489-024-05367-4 Y.-E. Hou, W. Gu, C. Wang, K. Yang, Y. Wang, “A Selection Hyper-heuristic based on Q-learning for School Bus Routing Problem,” International Journal of Applied Mathematics, no. 4, e35, Dec. 2022. N. Furian, M. O’Sullivan, C. Walker, E. Çela, “A machine learning-based branch and price algorithm for a sampled vehicle routing problem,” OR Spectrum, vol. 43, no. 3, pp. 693-732, Sep. 2021. https://doi.org/10.1007/s00291-020-00615-8 A. Yaddaden, S. Harispe, M. Vasquez, “Transfer Learning Helpful for Neural Combinatorial Optimization Applied to Vehicle Routing Problems?,” Computing and Informatics, vol. 41, pp. 172-190, 2022. https://doi.org/10.31577/cai_2022_1_172 M. Ben Hassine, M. S. Amri Sakhri, M. Tlili, “Machine learning approach to solving the capacitated vehicle routing problem,” in 1st IEEE Afro-Mediterranean Conference on Artificial Intelligence, AMCAI 2023 - Proceedings, 2023. https://doi.org/10.1109/AMCAI59331.2023.10431502 J. Dornemann, “Solving the capacitated vehicle routing problem with time windows via graph convolutional network assisted tree search and quantum-inspired computing,” Frontiers in Applied Mathematics and Statistics, vol. 9, e1155356, 2023. https://doi.org/10.3389/fams.2023.1155356 D. Weiss, K. Tierney, “Realtime gray-box algorithm configuration using cost-sensitive classification,” Annals of Mathematics and Artificial Intelligence, vol. 23, e9890, 2023. https://doi.org/10.1007/s10472-023-09890-x C. Wang, Z. Yu, S. McAleer, T. Yu, Y. Yang, “ASP: Learn a Universal Neural Solver!,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 6, pp. 4102-4114, Jun. 2024. https://doi.org/10.1109/TPAMI.2024.3352096 M. Morabit, G. Desaulniers, A. Lodi, “Learning to repeatedly solve routing problems,” Networks, vol. 83, no. 3, e22200, 2024. https://doi.org/10.1002/net.22200
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dc.rights.none.fl_str_mv	Mauricio Maca-Chagüendo, Carlos-Alberto Cobos-Lozada - 2024
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spelling	Maca-Chagüendo, MauricioCobos-Lozada, Carlos-Alberto2024-06-02 21:19:092026-04-16T17:47:43Z2024-06-02 21:19:092024-06-020121-1129https://repositorio.uptc.edu.co/handle/001/2160010.19053/uptc.01211129.v33.n68.2024.183792357-5328https://doi.org/10.19053/uptc.01211129.v33.n68.2024.18379application/pdftext/xmlUniversidad Pedagógica y Tecnológica de ColombiaMauricio Maca-Chagüendo, Carlos-Alberto Cobos-Lozada - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/18379Capacitated Vehicle Routing ProblemCVRPMetaheuristicsMachine learningArtificial neural networksReinforcement learningProblema de Enrutamiento de Vehículos con CapacidadMetaheurísticasAprendizaje de máquinaRedes neuronales artificialesAprendizaje por refuerzoA Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing ProblemsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85A. M. Altabeeb, A. M. Mohsen, A. Ghallab, “An improved hybrid firefly algorithm for capacitated vehicle routing problem,” Applied Software Computing, vol. 84, e105728, Nov. 2019. https://doi.org/10.1016/j.asoc.2019.105728A. M. Altabeeb, A. M. Mohsen, L. Abualigah, A. Ghallab, “Solving capacitated vehicle routing problem using cooperative firefly algorithm,” Applied Software Computing, vol. 108, e107403, Sep. 2021. https://doi.org/10.1016/j.asoc.2021.107403J. Cai, P. Wang, S. Sun, H. Dong, “A dynamic space reduction ant colony optimization for capacitated vehicle routing problem,” Software computing, vol. 26, no. 17, pp. 8745-8756, Sep. 2022. https://doi.org/10.1007/s00500-022-07198-2 .A. Thammano, P. Rungwachira, “Hybrid modified ant system with sweep algorithm and path relinking for the capacitated vehicle routing problem,” Heliyon, vol. 7, no. 9, e8029, Sep. 2021. https://doi.org/10.1016/j.heliyon.2021.e08029Y. Zhou, W. Li, X. Wang, Y. Qiu, W. Shen, “Adaptive gradient descent enabled ant colony optimization for routing problems,” Swarm and Evolutionary Computation, vol. 70, e101046, Apr. 2022. https://doi.org/10.1016/j.swevo.2022.101046P. Kalatzantonakis, A. Sifaleras, N. Samaras, “A reinforcement learning-Variable neighborhood search method for the capacitated Vehicle Routing Problem,” Expert Systems with Applications, vol. 213, e118812, Mar. 2023. https://doi.org/10.1016/j.eswa.2022.118812A. Hottung, K. Tierney, “Neural large neighborhood search for routing problems,” Artificial Intelligence, vol. 313, e103786, Dec. 2022. https://doi.org/10.1016/j.artint.2022.103786B. Kitchenham, P. Brereton, “A systematic review of systematic review process research in software engineering,” Information and Software Technology, vol. 55, no. 12, pp. 2049–2075, 2013. https://doi.org/10.1016/j.infsof.2013.07.010A. García-Holgado, S. Marcos-Pablos, F. J. García-Peñalvo, “Guidelines for performing systematic research projects reviews,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 05, e05, 2020. https://doi.org/10.9781/ijimai.2020.05.005W. Yang, L. Ke, “An improved fireworks algorithm for the capacitated vehicle routing problem,” Frontiers in Computer Science, vol. 13, no. 3, pp. 552-564, Jun. 2019. https://doi.org/10.1007/s11704-017-6418-9Y. Wu, F. Pan, S. Li, Z. Chen, M. Dong, “Peer-induced fairness capacitated vehicle routing scheduling using a hybrid optimization ACO–VNS algorithm,” Software computing, vol. 24, no. 3, pp. 2201-2213, Feb. 2020. https://doi.org/10.1007/s00500-019-04053-9Y. H. Jia, Y. Mei, M. Zhang, “A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem,” IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 10855-10868, Oct. 2022. https://doi.org/10.1109/TCYB.2021.3069942N. Frias, F. Johnson, C. Valle, “Hybrid Algorithms for Energy Minimizing Vehicle Routing Problem: Integrating Clusterization and Ant Colony Optimization,” IEEE Access, vol. 11, pp. 125800-125821, 2023. https://doi.org/10.1109/ACCESS.2023.3325787N. Sultana, J. Chan, T. Sarwar, A. K. Qin, “Learning to Optimise Routing Problems using Policy Optimisation,” in Proceedings of the International Joint Conference on Neural Networks, 2021. https://doi,org/10.1109/IJCNN52387.2021.9534010J. Li et al., “Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem,” IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 13572-13585, Dec. 2022. https://doi.org/10.1109/TCYB.2021.3111082Y. Wu, W. Song, Z. Cao, J. Zhang, A. Lim, “Learning Improvement Heuristics for Solving Routing Problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 5057-5069, Sep. 2022. https://doi.org/10.1109/TNNLS.2021.3068828Y. Xu, M. Fang, L. Chen, G. Xu, Y. Du, C. Zhang, “Reinforcement Learning with Multiple Relational Attention for Solving Vehicle Routing Problems,” IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 11107-11120, Oct. 2022. https://doi.org/10.1109/TCYB.2021.3089179K. Lei, P. Guo, Y. Wang, X. Wu, W. Zhao, “Solve routing problems with a residual edge-graph attention neural network,” Neurocomputing, vol. 508, pp. 79-98, Oct. 2022. https://doi.org/10.1016/j.neucom.2022.08.005Y. Hou, W. Gu, C. Wang, K. Yang, Y. Wang, “A Selection Hyper-heuristic based on Q-learning for School Bus Routing Problem,” International Journal of Applied Mathematics, vol. 52, no. 4, pp. 817-825, Dec. 2022.W. Yi, R. Qu, “Automated design of search algorithms based on reinforcement learning,” Information Sciences (NY), vol. 649, e119639, Nov. 2023. https://doi.org/10.1016/j.ins.2023.119639H. Liu, Z. Zong, Y. Li, D. Jin, “NeuroCrossover: An intelligent genetic locus selection scheme for genetic algorithm using reinforcement learning,” Applied Software Computing, vol. 146, e110680, Oct. 2023. https://doi.org/10.1016/j.asoc.2023.110680A. Bogyrbayeva, T. Yoon, H. Ko, S. Lim, H. Yun, C. Kwon, “A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone,” Transportation Research Part C: Emerging Technologies, vol. 148, e103981, Mar. 2023. https://doi.org/10.1016/j.trc.2022.103981W. Yi, R. Qu, L. Jiao, “Automated algorithm design using proximal policy optimisation with identified features,” Expert Systems with Applications, vol. 216, e119461, Apr. 2023. https://doi.org/10.1016/j.eswa.2022.119461W. Yi, R. Qu, L. Jiao, B. Niu, “Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, pp. 1072-1084, Aug. 2023. https://doi.org/10.1109/TEVC.2022.3197298D. H. Lee, J. Ahn, “Multi-start team orienteering problem for UAS mission re-planning with data-efficient deep reinforcement learning,” Applied Intelligence, vol. 54, no. 6, pp. 4467-4489, Mar. 2024. https://doi.org/10.1007/s10489-024-05367-4Y.-E. Hou, W. Gu, C. Wang, K. Yang, Y. Wang, “A Selection Hyper-heuristic based on Q-learning for School Bus Routing Problem,” International Journal of Applied Mathematics, no. 4, e35, Dec. 2022.N. Furian, M. O’Sullivan, C. Walker, E. Çela, “A machine learning-based branch and price algorithm for a sampled vehicle routing problem,” OR Spectrum, vol. 43, no. 3, pp. 693-732, Sep. 2021. https://doi.org/10.1007/s00291-020-00615-8A. Yaddaden, S. Harispe, M. Vasquez, “Transfer Learning Helpful for Neural Combinatorial Optimization Applied to Vehicle Routing Problems?,” Computing and Informatics, vol. 41, pp. 172-190, 2022. https://doi.org/10.31577/cai_2022_1_172M. Ben Hassine, M. S. Amri Sakhri, M. Tlili, “Machine learning approach to solving the capacitated vehicle routing problem,” in 1st IEEE Afro-Mediterranean Conference on Artificial Intelligence, AMCAI 2023 - Proceedings, 2023. https://doi.org/10.1109/AMCAI59331.2023.10431502J. Dornemann, “Solving the capacitated vehicle routing problem with time windows via graph convolutional network assisted tree search and quantum-inspired computing,” Frontiers in Applied Mathematics and Statistics, vol. 9, e1155356, 2023. https://doi.org/10.3389/fams.2023.1155356D. Weiss, K. Tierney, “Realtime gray-box algorithm configuration using cost-sensitive classification,” Annals of Mathematics and Artificial Intelligence, vol. 23, e9890, 2023. https://doi.org/10.1007/s10472-023-09890-xC. Wang, Z. Yu, S. McAleer, T. Yu, Y. Yang, “ASP: Learn a Universal Neural Solver!,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 6, pp. 4102-4114, Jun. 2024. https://doi.org/10.1109/TPAMI.2024.3352096M. Morabit, G. Desaulniers, A. Lodi, “Learning to repeatedly solve routing problems,” Networks, vol. 83, no. 3, e22200, 2024. https://doi.org/10.1002/net.22200e1837968e1837933Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/18379/15692https://revistas.uptc.edu.co/index.php/ingenieria/article/download/18379/15715Núm. 68 , Año 2024 : April-June 2024 (Continuous Publication)PublicationOREORE.xmltext/xml2712https://repositorio.uptc.edu.co/bitstreams/d7d6e768-a963-4079-a359-9bd8cf227433/download979acc16141ed65154d88842b70e0a4dMD51falseAnonymousREAD001/21600oai:repositorio.uptc.edu.co:001/216002026-10-29 14:05:21.492https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

A Systematic Review of Machine-Learning Enhanced Meta- Heuristics for Solving Capacitated Vehicle Routing Problems

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