Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimize total tardiness

The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the total tardiness criterion, which is aimed tow...

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Detalhes bibliográficos
Autores: Fernández-Viagas Escudero, Víctor, Valente, Jorge M.S., Framiñán Torres, José Manuel
Tipo de documento: artigo
Estado:Versión enviada para evaluación y publicación
Data de publicação:2018
País:España
Recursos:Universidad de Sevilla (US)
Repositório:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/95291
Acesso em linha:https://hdl.handle.net/11441/95291
https://doi.org/10.1016/j.eswa.2017.10.050
Access Level:Acceso aberto
Palavra-chave:Iterated greedy algorithm
Iterated local search
Beam search
Descrição
Resumo:The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the total tardiness criterion, which is aimed towards the satisfaction of customers in a make-to-order scenario. Although several approximate algorithms have been proposed for this problem in the literature, recent contributions for related problems suggest that there is room for improving the current available algorithms. Thus, our contribution is twofold: First, we propose a fast beam-search-based constructive heuristic that estimates the quality of partial sequences without a complete evaluation of their objective function. Second, using this constructive heuristic as initial solution, eight variations of an iterated-greedy-based algorithm are proposed. A comprehensive computational evaluation is performed to establish the efficiency of our proposals against the existing heuristics and metaheuristics for the problem.