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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Veröffentlicht in:Computers & operations research Jg. 38; H. 11; S. 1521 - 1533
Hauptverfasser: Minella, Gerardo, Ruiz, Rubén, Ciavotta, Michele
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.11.2011
Elsevier
Pergamon Press Inc
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2011.01.010