Algoritmo de enxame de abelhas para resolução do problema da programação da produção Job Shop flexível multiobjetivo

• Ano de defesa: 2017
• Autor(a) principal: Sanches, Rafael Francisco Viana
• Orientador(a): Kato, Edilson Reis Rodrigues
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Programação da produção; Multicritério; Enxame de abelhas; Mutação; Estruturas de vizinhança; Dominância de Pareto
• Palavras-chave em Inglês: Production scheduling; Multicriteria; Bee swarm; Makespan; Mutation; Neighborhood structures; Pareto dominance
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/9055

The production scheduling activity is considered as one of the most complex activities in production management. This activity is part of the class of NP-Hard problems found in the area of computer science, that is, those problems that can not be solved deterministically in polynomial time. In addition, the complexity of this activity may increase according to the constraints imposed on each programming system/problem. In this research, the problem of programming of production the Flexible Job Shop (JSF) is studied. This problem is considered an extension of the Job Shop programming problem. In JSF, a group of jobs (i.e., products, items, part of an item) formed by a set of operations and each operation must be programmed by a resource (i.e., machine) that belongs to a group of resources that have the same functional characteristics (e.g., cut, sanding, painting). This problem is characterized in two sub-problems being routing and sequencing activity. Routing involves determining which resource will process a given operation. Sequencing is the order in which each operation will be processed on a resource. Through established programming, the objective of this research is to optimize performance multicriteria: the makespan (i.e., time spent to produce a set of jobs), processing time spent on the resource that worked by more time and total production time. In order to reach the objectives mentioned above, a hybrid swarm approach is proposed in this research. In this approach, two auxiliary methods are used to treat the abovementioned sub-problems: genetic operator of mutation to perform the routing activity and for the sequencing activity, an adaptive method of neighborhood structures is proposed. In order to deal with the multiobjectivity of the problem, we propose the Pareto dominance method. Experimental results obtained through commonly used benchmarks prove the efficacy and superiority of the proposed approach when compared to other approaches also applied to the problem studied.