The flexible job shop scheduling problem with sequence flexibility and position-based learning effect

• Ano de defesa: 2024
• Autor(a) principal: Araújo, Kennedy Anderson Guimarães de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Constraint programming; Efeito de aprendizagem; Flexibilidade de roteamento; Flexibilidade de sequenciamento; Integer linear programming; Job shop; Learning effect; Makespam; Makespan; Metaheurísticas; Metaheuristics; Programação linear inteira; Programação por restrições; Routing flexibility; Sequencing flexibility
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/45/45132/tde-25042024-151416/

This work addresses the flexible job shop scheduling problem with sequencing flexibility and position-based learning effect. In this variant of the flexible job shop scheduling problem, precedence constraints of the operations constituting a job are given by an arbitrary directed acyclic graph, in opposition to the classical case in which a total order is imposed. Additionally, it is assumed that the processing time of an operation in a machine is subject to a learning process such that the larger the position of the operation in the machine, the faster the operation is processed. The problem considered corresponds to modern problems of great relevance in the printing industry. Mixed integer programming and constraint programming models are presented and compared in the present work. In addition, constructive heuristics are introduced to provide an initial solution to the models\' solvers. As an alternative to the models, a local search method and four trajectory metaheuristics are considered. In the local search, we show that the classical strategy of only reallocating operations that are part of the critical path can miss better quality neighbors, as opposed to what happens in the case where there is no learning effect. Consequently, we analyze an alternative type of neighborhood reduction that eliminates only neighbors that are not better than the current solution. In addition, we also suggest a neighborhood cut and experimentally verify that this significantly reduces the neighborhood size, bringing efficiency, with minimal loss in effectiveness. Sets of benchmark instances are also introduced. Extensive numerical experiments with the proposed methods are carried on. The experiments show that simulating annealing, tabu search with reduced neighborhood and iterated local search with cropped neighborhood, built on the introduced local search, stands out in solution quality All the methods introduced, as well as the instances and solutions found, are freely available.