Meta-heurísticas SA e Clustering Search aplicada ao problema flexivel Job Shop Scheduling com restrições de trabalhadores e com tempos de setup antecipados

Detalhes bibliográficos
Ano de defesa: 2021
Autor(a) principal: Altoé, Wagner Amorim da Silva
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Mestrado em Informática
Centro Tecnológico
UFES
Programa de Pós-Graduação em Informática
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
SA
CS
Link de acesso: http://repositorio.ufes.br/handle/10/15488
Resumo: The problem known as Worker Constrained Flexible Job Shop Scheduling Problem With sequence-dependent setup times (WSFJSP-SDST), is an extension of the problem Job Shop Schedulling (JSP). In this production environment, machines are operated by workers to process a set of jobs. A job is characterized by having a fixed order of operations, where each operation can only be processed by workers who have the ability to perform them using a suitable machine. Each worker can only execute a maximum of one operation at a time, as well as, each machine can only be operated by one worker at a time, respecting the restriction that, when an operation is started, it cannot be interrupted before its completion. In addition, it is considered that for an operation to be performed on a machine, time is needed to prepare the machine to be used. This work describes the Simulated Annealing (SA) and Clustering Search (CS) metaheuristics to solve the WSFJSP-SDST. This work describes the Simulated Annealing (SA) and Clustering Search (CS) metaheuristics to solve the WSFJSP-SDST. Meta-heuristics were tested with instances of a real company taken from the literature, as well as with instances generated by this study. Computational experiments show that the proposed algorithms enabled the generation of higher quality solutions with reduced computational cost.