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dc.contributor.advisorSembiring, Meilita Tryana
dc.contributor.advisorNazaruddin
dc.contributor.authorZuya, Novika
dc.date.accessioned2025-02-26T01:20:33Z
dc.date.available2025-02-26T01:20:33Z
dc.date.issued2024
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/101622
dc.description.abstractOptimal shipping allocation planning is an essential element in increasingly complex and dynamic supply chain management. Shipping route optimization can provide competitive advantage by minimizing operational costs through freight utilization and increasing customer satisfaction. This study proposes a shipping allocation model designed to solve the Capacitated Vehicle Routing Problem (CVRP) using Genetic Algorithm (GA) implemented in the Multi Objective Evolutionary Algorithm (MOEA) Framework. This model aims to solve the CVRP by considering heterogeneous fleet, product variability and different delivery destinations. The chromosome representation in the model describes the order of customer visits by the available fleet, while the fitness function is used to minimize the total distance traveled in order to maximize the efficiency of vehicle capacity utilization. The results show that this model is able to increase delivery efficiency, with load utilization of 99% with all requests allocateden_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectCapacitated Vehicle Routing Problemen_US
dc.subjectDelivery Allocationen_US
dc.subjectFreight Utilizationen_US
dc.subjectGenetic Algorithmen_US
dc.subjectHeterogenoeus Fleeten_US
dc.subjectModelen_US
dc.titleRancangan Model Alokasi Pengiriman Produk Karton Bergelombang dengan Mempertimbangkan Heterogeneous Fleet, Multi Product dan Clusteringen_US
dc.title.alternativeDesign of Corrugated Cardboard Product Delivery Allocation Model by Considering Heterogeneous Fleet, Multi Product and Clusteringen_US
dc.typeThesisen_US
dc.identifier.nimNIM227025013
dc.identifier.nidnNIDN0003057003
dc.identifier.nidnNIDN0001086008
dc.identifier.kodeprodiKODEPRODI26101#Teknik Industri
dc.description.pages82 Pagesen_US
dc.description.typeTesis Magisteren_US
dc.subject.sdgsSDGs 9. Industry Innovation And Infrastructureen_US


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