An adaptive multi-objective algorithm based on decomposition and large neighborhood search for a green machine scheduling problem.

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dc.contributor.authorCota, Luciano Perdigão
dc.contributor.authorGuimarães, Frederico Gadelha
dc.contributor.authorRibeiro, Roberto Gomes
dc.contributor.authorMeneghini, Ivan Reinaldo
dc.contributor.authorOliveira, Fernando Bernardes de
dc.contributor.authorSouza, Marcone Jamilson Freitas
dc.contributor.authorSiarry, Patrick
dc.date.accessioned2020-07-24T18:39:35Z
dc.date.available2020-07-24T18:39:35Z
dc.date.issued2019
dc.description.abstractGreen machine scheduling consists in the allocation of jobs in order to maximize production, in view of the sustainable use of energy. This work addresses the unrelated parallel machine scheduling problem with setup times, with the minimization of the makespan and the total energy consumption. The latter takes into account the power consumption of each machine in different operation modes. We propose multi-objective extensions of the Adaptive Large Neighborhood Search (ALNS) metaheuristic with Learning Automata (LA) to improve the search process and to solve the large scale instances efficiently. ALNS combines ad-hoc destroy and repair (also named removal and insertion) operators and a local search procedure. The LA is used to adapt the selection of insertion and removal operators within the framework of ALNS. Two new algorithms are developed: the MO-ALNS and the MO-ALNS/D. The first algorithm is a direct extension of single objective ALNS by using multi-objective local search. As this method does not offer much control of the diversification of the Pareto front approximation, a second strategy employs the decomposition approach similar to MOEA/D algorithm. The results show that the MO-ALNS/D algorithm has better performance than MO-ALNS and MOEA/D in all indicators. These findings show that the decomposition strategy is beneficial not only for evolutionary algorithms, but it is indeed an efficient way to extend ALNS to multi-objective problems.pt_BR
dc.identifier.citationCOTA, L. P. et al. An adaptive multi-objective algorithm based on decomposition and large neighborhood search for a green machine scheduling problem. Swarm and Evolutionary Computation, v. 51, n. 100601, dez. 2019. Disponível em: <https://www.sciencedirect.com/science/article/abs/pii/S2210650219301130>. Acesso em: 18 jun. 2020.pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.swevo.2019.100601pt_BR
dc.identifier.issn2210-6502
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/12502
dc.identifier.uri2https://www.sciencedirect.com/science/article/abs/pii/S2210650219301130pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectParallel machinespt_BR
dc.subjectAdaptive large neighborhood searchpt_BR
dc.subjectLearning automatapt_BR
dc.subjectDecomposition and aggregationpt_BR
dc.titleAn adaptive multi-objective algorithm based on decomposition and large neighborhood search for a green machine scheduling problem.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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