An Improved GreyWolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

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dc.contributor.authorAlejo-Reyes, Avelina
dc.contributor.authorCuevas, Erik
dc.contributor.authorMendoza, Abraham
dc.contributor.authorOlivares-Benitez, Elias
dc.contributor.authorRodríguez-Vázquez, Alma N.
dc.date.accessioned2024-04-03T22:58:20Z
dc.date.available2024-04-03T22:58:20Z
dc.date.issued2020-08
dc.descriptionSupplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers di erent elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve di cult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a di cult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.es_MX
dc.description.sponsorshipITESO, A.C.es
dc.identifier.citationAlejo-Reyes, A.; Cuevas, E.; Rodríguez, A.; Mendoza, A.; Olivares-Benitez, E. An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem. Mathematics 2020, 8, 1457.es_MX
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/11117/10852
dc.language.isoenges_MX
dc.publisherMDPIes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectMetaheuristic Algorithmses_MX
dc.subjectGrey Wolf Optimizeres_MX
dc.subjectSupply Chain Managementes_MX
dc.titleAn Improved GreyWolf Optimizer for a Supplier Selection and Order Quantity Allocation Problemes_MX
dc.typeinfo:eu-repo/semantics/articlees_MX
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_MX

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