Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems

Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the...

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Published in:	Computers & operations research Vol. 38; no. 11; pp. 1521 - 1533
Main Authors:	Minella, Gerardo, Ruiz, Rubén, Ciavotta, Michele
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.11.2011 Elsevier Pergamon Press Inc
Subjects:	Algorithms Applied sciences Decision theory. Utility theory Exact sciences and technology Flowshop Greedy algorithms Inventory control, production control. Distribution Iterated Greedy Iterative methods Job shops Mathematical problems Multi-objective Operational research and scientific management Operational research. Management science Operations management Operations research Optimization algorithms Pareto optimality Pareto optimum Permutations Production scheduling Scheduling Scheduling, sequencing Searching State of the art Studies Iterated Greedy Scheduling Flowshop Multi-objective Local search Population management Pareto optimum Permutation Empirical method Multiobjective programming Makespan Delay Production management Statistical test Greedy algorithm Flow shop Impulse
ISSN:	0305-0548, 1873-765X, 0305-0548
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Abstract	Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the most widely studied scheduling settings. Recently, the Iterated Greedy methodology for solving the single-objective version of the flowshop problem has produced state-of-the-art results. This paper proposes a new algorithm based on Iterated Greedy technique for solving the multi-objective permutation flowshop problem. This algorithm is characterised by an effective initialisation of the population, management of the Pareto front, and a specially tailored local search, among other things. The proposed multi-objective Iterated Greedy method is shown to outperform other recent approaches in comprehensive computational and statistical tests that comprise a large number of instances with objectives involving makespan, tardiness and flowtime. Lastly, we use a novel graphical tool to compare the performances of stochastic Pareto fronts based on Empirical Attainment Functions.
AbstractList	Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the most widely studied scheduling settings. Recently, the Iterated Greedy methodology for solving the single-objective version of the flowshop problem has produced state-of-the-art results. This paper proposes a new algorithm based on Iterated Greedy technique for solving the multi-objective permutation flowshop problem. This algorithm is characterised by an effective initialisation of the population, management of the Pareto front, and a specially tailored local search, among other things. The proposed multi-objective Iterated Greedy method is shown to outperform other recent approaches in comprehensive computational and statistical tests that comprise a large number of instances with objectives involving makespan, tardiness and flowtime. Lastly, we use a novel graphical tool to compare the performances of stochastic Pareto fronts based on Empirical Attainment Functions. [PUBLICATION ABSTRACT] Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the most widely studied scheduling settings. Recently, the Iterated Greedy methodology for solving the single-objective version of the flowshop problem has produced state-of-the-art results. This paper proposes a new algorithm based on Iterated Greedy technique for solving the multi-objective permutation flowshop problem. This algorithm is characterised by an effective initialisation of the population, management of the Pareto front, and a specially tailored local search, among other things. The proposed multi-objective Iterated Greedy method is shown to outperform other recent approaches in comprehensive computational and statistical tests that comprise a large number of instances with objectives involving makespan, tardiness and flowtime. Lastly, we use a novel graphical tool to compare the performances of stochastic Pareto fronts based on Empirical Attainment Functions.
Author	Minella, Gerardo Ciavotta, Michele Ruiz, Rubén
Author_xml	– sequence: 1 givenname: Gerardo surname: Minella fullname: Minella, Gerardo email: mgerar@iti.upv.es – sequence: 2 givenname: Rubén surname: Ruiz fullname: Ruiz, Rubén email: rruiz@eio.upv.es – sequence: 3 givenname: Michele surname: Ciavotta fullname: Ciavotta, Michele email: mciavotta@iti.upv.es
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Snippet	Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective...
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SubjectTerms	Algorithms Applied sciences Decision theory. Utility theory Exact sciences and technology Flowshop Greedy algorithms Inventory control, production control. Distribution Iterated Greedy Iterative methods Job shops Mathematical problems Multi-objective Operational research and scientific management Operational research. Management science Operations management Operations research Optimization algorithms Pareto optimality Pareto optimum Permutations Production scheduling Scheduling Scheduling, sequencing Searching State of the art Studies
Title	Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems
URI	https://dx.doi.org/10.1016/j.cor.2011.01.010 http://www.econis.eu/PPNSET?PPN=656920920 https://www.proquest.com/docview/864098222 https://www.proquest.com/docview/1753530158 https://www.proquest.com/docview/889391539
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