Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse
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| Format: | Artículo científico |
| Langue: | en |
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Universidad de Antioquia
2016
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| author | Rodrigo Andrés Gómez-Montoya |
| author_facet | Rodrigo Andrés Gómez-Montoya |
| contents | Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse Rodrigo Andrés Gómez-Montoya Alexander Alberto Correa-Espinal José Daniel Hernández-Vahos Ingeniería Picking genetic algorithm refrigerated warehouse PSO (Particle Swarm Optimization) discrete 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. 2016 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43047073002 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.80 |
| format | Artículo científico |
| id | redalyc_43047073002 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Universidad de Antioquia |
| spellingShingle | Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse Rodrigo Andrés Gómez-Montoya Ingeniería Picking genetic algorithm refrigerated warehouse PSO (Particle Swarm Optimization) discrete Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse Rodrigo Andrés Gómez-Montoya Alexander Alberto Correa-Espinal José Daniel Hernández-Vahos Ingeniería Picking genetic algorithm refrigerated warehouse PSO (Particle Swarm Optimization) discrete 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. 2016 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43047073002 en http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.80 |
| title | Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse |
| topic | Ingeniería Picking genetic algorithm refrigerated warehouse PSO (Particle Swarm Optimization) discrete |
| url | https://www.redalyc.org/articulo.oa?id=43047073002 |