Implementation of an Artificial Bee Colony to Solve an Order Picking Problem
The artificial bee colony (ABC) algorithm is an optimization method based on swarm intelligence which has demonstrated to be capable of obtaining satisfactory results on a diversity of optimization problems. However, the implementation of this optimization method hasn't been much explored on or...
| Autores: | , , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | México |
| Institución: | Universidad Autónoma de Ciudad Juárez |
| Repositorio: | Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez |
| OAI Identifier: | oai:uacj.mx:oai:cathi.uacj.mx:20.500.11961ir-8729 |
| Acceso en línea: | https://doi.org/10.4018/978-1-5225-8131-4.ch007 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial bee colony info:eu-repo/classification/cti/1 |
| Sumario: | The artificial bee colony (ABC) algorithm is an optimization method based on swarm intelligence which has demonstrated to be capable of obtaining satisfactory results on a diversity of optimization problems. However, the implementation of this optimization method hasn't been much explored on order picking problems, even though order picking represents up to 55% of the total operational cost of a typical warehouse. The order picking problem has even more importance on nonprofit organizations like food banks since they operate with a limited budget. In this chapter, the authors implemented an ABC algorithm to solve the order picking problem within a food bank. The goal was to determine which parameter values contribute the most during the optimization process. Experiments were conducted using nine sets of parameters for the ABC; results show that the approach is suitable for the study case. |
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