Multi-objective meta-heuristics for a scheduling problem with sequence-dependent machine deterioration

Vol 54, 2022 - 152781
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Resumo

This paper addresses a scheduling problem considering sequence-dependent machine deterioration, with the criterion of minimizing the makespan and total tardiness simultaneously. To handle this problem, we compare two different approaches to extending Iterated Local Search (ILS) meta-heuristic to multi-objective problems. In this paper, we develop the Pareto Iterated Local Search (PILS), which is a direct extension of the Pareto Local Search (PLS) heuristic that uses multi-objective local search. On the other hand, we use a hybrid meta-heuristic algorithm, named ILS/D, based on the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and on the ILS. This paper aims to evaluate the performance of the algorithm used to solve the problem addressed, in an algorithm based only on local search meta-heuristic, since the problem has already been evaluated in algorithms based on population and hybrid meta-heuristics. Numerical experiments are carried out on test problems with different scales.

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Instituições
  • 1 Instituto Federal de Educação, Ciência e Tecnologia do Norte de Minas Gerais
  • 2 Universidade Federal dos Vales do Jequitinhonha e Mucuri
  • 3 Braude College of Engineering
  • 4 Universidade Federal de Minas Gerais
Eixo Temático
  • 13 - MH – Metaheurísticas
Palavras-chave
Machine deterioration
Sequence-depended
Multi-Objective Optimization