Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect
Mixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a comm...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Institución: | Associação Brasileira de Engenharia de Produção (ABEPRO) |
| Repositorio: | Brazilian Journal of Operations & Production Management (Online) |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs.bjopm.org.br:article/414 |
| Acceso en línea: | https://bjopm.org.br/bjopm/article/view/414 |
| Access Level: | acceso abierto |
| Palabra clave: | Mixed-model assembly line balancing learning effect parallel line |
| Sumario: | Mixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a common line and the other is an express line due to more modern technology or operators with higher skills. Therefore, the cost of equipment and skilled labor in the express line is higher, and also, the learning effect on resource dependent task times and setup times is considered in the assemble-to-order environment. The aim of this study is to minimize the cycle time and the total operating cost and smoothness index by configuration of tasks in stations, according to their precedence diagrams. Also, assigning the assistants to some tasks in some stations and for some models is allowed. This problem is categorized as an NP-hard problem and for solving this multi-objective problem, non-dominated sorting genetic algorithm ІІ (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied. Finally, for comparing the proposed methods some numerical examples are implemented and the result show that MOPSO outperforms NSGAII. |
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