Informática

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Programa de Pós-Graduação em Informática

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Navegando Informática por Autor "Altoé, Wagner Amorim da Silva"

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    Meta-heurísticas SA e Clustering Search aplicada ao problema flexivel Job Shop Scheduling com restrições de trabalhadores e com tempos de setup antecipados
    (Universidade Federal do Espírito Santo, 2021-03-03) Altoé, Wagner Amorim da Silva; Amaral, André Renato Sales; https://orcid.org/0000-0001-7344-3994; http://lattes.cnpq.br/4695002674556067; http://lattes.cnpq.br/1735545220047375; Mauri, Geraldo Regis; https://orcid.org/0000-0002-8393-7741; http://lattes.cnpq.br/7870111209439581; Lorenzoni, Luciano Lessa; https://orcid.org/0000-0003-4859-7750; http://lattes.cnpq.br/7959495705859101
    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.