{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:29:02Z","timestamp":1779384542503,"version":"3.53.1"},"reference-count":16,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Efficient sales route optimization is a critical challenge in logistics and distribution, especially under real-world conditions involving traffic variability and dynamic constraints. This study proposes a novel Hybrid Genetic Algorithm (GAAM-TS) that integrates Adaptive Mutation, Tabu Search, and an LSTM-based travel time prediction model to enable real-time, intelligent route planning. The approach addresses the limitations of traditional genetic algorithms by enhancing solution quality, maintaining population diversity, and incorporating data-driven traffic estimations via deep learning. Experimental results on real-world data from the NYC Taxi dataset show that GAAM-TS significantly outperforms both Standard GA and GA-AM variants, achieving up to 20% improvement in travel efficiency while maintaining robustness across problem sizes. Although GAAM-TS incurs higher computational costs, it is best suited for offline or batch optimization scenarios, whereas GA-AM provides a balanced alternative for near-real-time applications. The proposed methodology is applicable to last-mile delivery, fleet routing, and sales territory management, offering a scalable and adaptive solution. Future work will explore parallelization strategies and multi-objective extensions for sustainability-aware routing.<\/jats:p>","DOI":"10.3390\/a18050260","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T06:49:02Z","timestamp":1746082142000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Advanced Sales Route Optimization Through Enhanced Genetic Algorithms and Real-Time Navigation Systems"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3927-5146","authenticated-orcid":false,"given":"Wilmer Clemente","family":"Cunuhay Cuchipe","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Applied Sciences, Technical University of Cotopaxi, La Man\u00e1 Extension, La Man\u00e1 050201, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2983-2508","authenticated-orcid":false,"given":"Johnny Baja\u00f1a","family":"Zajia","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Sciences, Technical University of Cotopaxi, La Man\u00e1 Extension, La Man\u00e1 050201, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5366-5917","authenticated-orcid":false,"given":"Byron","family":"Oviedo","sequence":"additional","affiliation":[{"name":"Faculty of Graduate Programs, State Technical University of Quevedo, Quevedo 120503, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8568-8024","authenticated-orcid":false,"given":"Cristian","family":"Zambrano-Vega","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Sciences, State Technical University of Quevedo, Quevedo 120503, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,1]]},"reference":[{"key":"ref_1","unstructured":"Garey, M.R., and Johnson, D.S. (1997). Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1023\/A:1006529012972","article-title":"Genetic algorithms for the travelling salesman problem: A review of representations and operators","volume":"13","author":"Kuijpers","year":"1999","journal-title":"Artif. Intell. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/0377-2217(92)90192-C","article-title":"The vehicle routing problem: An overview of exact and approximate algorithms","volume":"59","author":"Laporte","year":"1992","journal-title":"Eur. J. Oper. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/4235.585892","article-title":"Ant colony system: A cooperative learning approach to the traveling salesman problem","volume":"1","author":"Dorigo","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_5","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1287\/ijoc.1.3.190","article-title":"Tabu search\u2014Part I","volume":"1","author":"Glover","year":"1989","journal-title":"ORSA J. Comput."},{"key":"ref_7","first-page":"116854","article-title":"Adaptive genetic algorithm for dynamic vehicle routing problem with time windows","volume":"198","author":"Liu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zambrano-Vega, C., Acosta, G., Loor, J., Su\u00e1rez, B., Jaramillo, C., and Oviedo, B. (2019). A sales route optimization mobile application applying a genetic algorithm and the Google Maps navigation system. Information Technology and Systems: Proceedings of ICITS 2019, Springer International Publishing. Advances in Intelligent Systems and Computing.","DOI":"10.1007\/978-3-030-11890-7_50"},{"key":"ref_9","first-page":"106024","article-title":"A hybrid genetic algorithm with tabu search for vehicle routing problem","volume":"137","author":"Wang","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_10","first-page":"102621","article-title":"A deep learning approach for travel time prediction in urban areas: A case study in Beijing","volume":"115","author":"Zhang","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sui, J., Ding, S., Huang, X., Yu, Y., Liu, R., Xia, B., Ding, Z., Xu, L., Zhang, H., and Yu, C. (2025). A survey on deep learning-based algorithms for the traveling salesman problem. Front. Comput. Sci., 19.","DOI":"10.1007\/s11704-024-40490-y"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cai, Y., and Chen, H. (2025). An improved salp swarm algorithm for permutation flow shop vehicle routing problem. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-86054-3"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bolotbekova, A., Hakli, H., and Beskirli, A. (2025). Trip route optimization based on bus transit using genetic algorithm with different crossover techniques: A case study in Konya\/T\u00fcrkiye. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-86695-4"},{"key":"ref_14","first-page":"1631","article-title":"Optimization of Sustainable Vehicle Routing Problem Taking into Account Social Utility and Employing a Strategy with Multiple Objectives","volume":"38","author":"Gouraji","year":"2025","journal-title":"Int. J. Eng. Trans. B Appl."},{"key":"ref_15","first-page":"196","article-title":"Optimization of vehicle routing problems combining the demand urgency and road damage for multiple disasters","volume":"6","author":"Li","year":"2025","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_16","unstructured":"NYC Taxi and Limousine Commission (2025, February 27). New York City Taxi Trip Data. Available online: https:\/\/aws.amazon.com\/marketplace\/pp\/prodview-okyonroqg5b2u#resources."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/5\/260\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:25:45Z","timestamp":1760030745000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/5\/260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":16,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["a18050260"],"URL":"https:\/\/doi.org\/10.3390\/a18050260","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,1]]}}}