An iterated local search for the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times

[Display omitted] •The multiple objectives and the sequence-dependent setup times are considered in the permutation flowshop scheduling problem.•The extension of conventional single-objective iterated local search (ILS) to solve multi-objective combinatorial optimization problem.•A Pareto based vari...

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Bibliographic Details
Published in:	Applied soft computing Vol. 52; pp. 39 - 47
Main Authors:	Xu, Jianyou, Wu, Chin-Chia, Yin, Yunqiang, Lin, Win-Chin
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.03.2017
Subjects:	Iterated local search Multi-objective optimization Permutation flowshop scheduling with sequence-dependent setup times Multi-objective optimization Iterated local search Permutation flowshop scheduling with sequence-dependent setup times
ISSN:	1568-4946, 1872-9681
Online Access:	Get full text
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Summary:	[Display omitted] •The multiple objectives and the sequence-dependent setup times are considered in the permutation flowshop scheduling problem.•The extension of conventional single-objective iterated local search (ILS) to solve multi-objective combinatorial optimization problem.•A Pareto based variable depth search is designed to act as the multi-objective local search phase in the multi-objective ILS.•The experimental results on some benchmark problems show that the proposed multi-objective ILS outperforms several powerful multi-objective evolutionary algorithms in the literature.•A multi-objective iterated local search is proposed. Due to its simplicity yet powerful search ability, iterated local search (ILS) has been widely used to tackle a variety of single-objective combinatorial optimization problems. However, applying ILS to solve multi-objective combinatorial optimization problems is scanty. In this paper we design a multi-objective ILS (MOILS) to solve the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times to minimize the makespan and total weighted tardiness of all jobs. In the MOILS, we design a Pareto-based variable depth search in the multi-objective local search phase. The search depth is dynamically adjusted during the search process of the MOILS to strike a balance between exploration and exploitation. We incorporate an external archive into the MOILS to store the non-dominated solutions and provide initial search points for the MOILS to escape from local optima traps. We compare the MOILS with several multi-objective evolutionary algorithms (MOEAs) shown to be effective for treating the multi-objective permutation flowshop scheduling problem in the literature. The computational results show that the proposed MOILS outperforms the MOEAs.
ISSN:	1568-4946 1872-9681
DOI:	10.1016/j.asoc.2016.11.031