Artículo
A Hybrid Metaheuristic for the Omnichannel Multiproduct Inventory Replenishment Problem
Autor/es | Lorenzo Espejo, Antonio
Muñuzuri, Jesús Guadix Martín, José Escudero Santana, Alejandro |
Departamento | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II |
Fecha de publicación | 2022-04 |
Fecha de depósito | 2022-05-25 |
Publicado en |
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Resumen | In the current paradigm for the retail industry, which is experiencing a rapid evolution, especially in textile companies, the generic problem of product allocation in a distribution and supply chain consisting of one main ... In the current paradigm for the retail industry, which is experiencing a rapid evolution, especially in textile companies, the generic problem of product allocation in a distribution and supply chain consisting of one main warehouse and several locations, belonging to different sales channels, is a challenge. The omnichannel replenishment process focuses on dynamically optimizing a shop or intermediate warehouse inventory for a wide range of products based on a forecast of sales, in order to fulfill the demand of all of the channels considered. In this context, the aims of this work were (a) to optimize inventory replenishment for multiple channels and products that are not perishable but devalue over time, and (b) to implement a methodology that combines the benefits of the Particle Swarm Optimization metaheuristic and Simulated Annealing. This study was carried out for different sales periods, channels and product configurations by performing a sensitivity analysis between the way new solutions are updated and the degree of intensification used in local search. |
Identificador del proyecto | TRACSINT P20_01183
FPU20/05584 |
Cita | Lorenzo Espejo, A., Muñuzuri, J., Guadix Martín, J. y Escudero Santana, A. (2022). A Hybrid Metaheuristic for the Omnichannel Multiproduct Inventory Replenishment Problem. Journal of Theoretical and Applied Electronic Commerce Research, 17 (2), 476-492. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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JTACR_muñuzuri_2022_hybrid.pdf | 1.283Mb | [PDF] | Ver/ | |