Logistics Optimization Using Hybrid Genetic Algorithm (HGA): A Solution to the Vehicle Routing Problem With Time Windows (VRPTW)

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dc.authorid0000-0003-2966-6060en_US
dc.authorid0000-0002-8098-3611en_US
dc.authorid0000-0002-5316-8101en_US
dc.contributor.authorMaroof, Ayesha
dc.contributor.authorAyvaz, Berk
dc.contributor.authorNaeem, Khawar
dc.date.accessioned2024-03-22T07:29:36Z
dc.date.available2024-03-22T07:29:36Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractThe Vehicle Routing Problem with Time Windows (VRPTW) is paramount in elevating operational efficiency, driving cost reductions, and enhancing customer satisfaction. It is a renowned challenge with diverse real-world applications, where the core objective is determining the most efficient routes for a fleet of vehicles. This research introduces a cutting-edge Hybrid Genetic Algorithm-Solomon Insertion Heuristic (HGA-SIH) solution, reinforced by the powerful Solomon Insertion constructive heuristic to solve the VRPTW as an NP-hard problem. The performance of the proposed HGA-SIH is validated against Solomon's VRPTW benchmark instances. The results showcase the outstanding performance of HGA, achieving Best-Known Solutions (BKS) for 11 instances and enhancing BKS solutions in one instance. Experimental findings validate that HGA-SIH consistently delivers results on par with or surpasses those obtained by several cutting-edge algorithms when evaluated based on various solution quality metrics. HGA-SIH consistently excels in efficiently managing the number of vehicles while minimizing travel distances, resulting in slight deviations from BKS that remain within practical limits. The research highlights the adaptability and efficacy of HGA-SIH in addressing a wide range of VRPTW scenarios, thereby making substantial contributions to logistics and supply chain optimization.en_US
dc.identifier.doi10.1109/ACCESS.2024.3373699en_US
dc.identifier.endpage36989en_US
dc.identifier.scopus2-s2.0-85187374983en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage36974en_US
dc.identifier.urihttps://hdl.handle.net/11467/7174
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3373699
dc.identifier.volume12en_US
dc.identifier.wosWOS:001185001600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHybrid Genetic Algorithm (HGA), logistics and transportation, Solomon Insertion Heuristic, supply chain optimization, vehicle routing problem with time windows (VRPTW)en_US
dc.titleLogistics Optimization Using Hybrid Genetic Algorithm (HGA): A Solution to the Vehicle Routing Problem With Time Windows (VRPTW)en_US
dc.typeArticleen_US

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