Effort Optimization Model in Warehouse Manual Picking. Case Study: Tusan Distribuidores

• Autores: González Cáceres, Mateo Antonio, Tang Yep, Sebastian
• Formato: tesis de grado
• Fecha de Publicación: 2022
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/17465
• Enlace del recurso: https://hdl.handle.net/20.500.12724/17465
• Nivel de acceso: acceso abierto
• Materia: Almacenes; Trabajadores; Sistemas de preparación de pedidos; Distribución comercial; Sistema musculoesquelético; Estudios de prefactibilidad; Ergonomía; Warehouses; Employees; Order picking systems; Physical distribution of goods; Musculoskeletal system; Human engineering; Prefeasibility studies; https://purl.org/pe-repo/ocde/ford#2.11.04

A recurring and expensive operation in company’s warehouses is the picking. Operators who execute this activity perform repetitive physical movements and carry objects constantly, which can trigger musculoskeletal disorders. Adittionally, there is a great consumption of energy by the operators, which could have negative consequences in the long term. Health damages could reduce the quality of the company’s service and productivity due to the increase in absenteeism of the personnel because of the recovery. The objective of the investigation is the application of an optimization model to reduce energy consumption during picking through the redistribution of product lines within a warehouse. The warehouse under study belongs to a Peruvian distributor with several lines of products for mass consumption. The applied model will be the one proposed by Diefenbach & Glock. The data of the current situation taken were: demand, average weights per line, efforts, distances and locations, which were coded in “A Mathematical Programming Language” (AMPL) and solved using the CPLEX solver. The results show 4.4% reduction in energy expenditure compared to the current distribution and 6.0% compared to randomly generated distributions. Finally, the optimal distribution by product lines is presented as a result of the modelling that reduces effort.