SciELO - Scientific Electronic Library Online

 
 número80The perception of colombians about science and technology according to their education level: professional and non-professional population índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Não possue artigos similaresSimilares em SciELO
  • Em processo de indexaçãoSimilares em Google

Compartilhar


Revista Facultad de Ingeniería Universidad de Antioquia

versão impressa ISSN 0120-6230

Resumo

GOMEZ-MONTOYA, Rodrigo Andrés; CORREA-ESPINAL, Alexander Alberto  e  HERNANDEZ-VAHOS, José Daniel. Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse. Rev.fac.ing.univ. Antioquia [online]. 2016, n.80, pp.9-20. ISSN 0120-6230.  https://doi.org/10.17533/udea.redin.n80a02.

This paper aims at formulating a Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse (PRPHE). Discrete particle swarm optimization (PSO) and genetic algorithm (GA) metaheuristics are developed and validated for solving PRPHE. The discrete PSO is a novel approach to solving cold routing picking problems, which has not been detected in the scientific literature and is considered a contribution to the state of the art. The main difference between classical and discrete PSO is the structure and algebraic formulation of the positions and velocities of the particles, which are discrete rather than continuous under our approach. A full factorial design was developed with the following four factors: picking routing metaheuristic (PRM), depot, picking list size (PLS) and homogeneous material handling equipment (MHE). Based on the results of the experimental analysis, we identified that GA metaheuristics generated better solutions than discrete PSO for PRPHE. These statistical results demonstrated that GA metaheuristics produced time savings of between 22.89 and 86.75 seconds per set of cold picking routes, as well as an increase in the operational efficiency of between 1.98 and 2.81%, as compared with PSO discrete. Finally, it should be noted that this paper is one of the first in tackling picking routing in a refrigerated warehouse, thereby contributing to knowledge in this field.

Palavras-chave : Picking; PSO (Particle Swarm Optimization) discrete; genetic algorithm; refrigerated warehouse Preparación de pedidos; PSO (Particle Swarm Optimization) discreto; algoritmo genético; almacén refrigerado.

        · resumo em Espanhol     · texto em Inglês     · Inglês ( pdf )