Hybrid Flow-Shop: a Memetic Algorithm Using Constraint-Based Scheduling for Efficient Search

The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the implementation details of a genetic algorithm, which is used in the mem...

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Veröffentlicht in:Journal of mathematical modelling and algorithms Jg. 8; H. 3; S. 271 - 292
Hauptverfasser: Jouglet, Antoine, Oğuz, Ceyda, Sevaux, Marc
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.08.2009
Springer Nature B.V
Springer Verlag
Schlagworte:
Algorithms
Computer Science
Data Mining and Knowledge Discovery
Genetic algorithms
Management Science
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Multiprocessing
Mutation
Operations Research
Optimization
Performance evaluation
Scheduling
Studies
Multiprocessor task scheduling
Memetic algorithm
Hybrid flow-shop
Genetic algorithm
Constraint programming
ISSN:1570-1166, 2214-2487, 1572-9214, 2214-2495
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Zusammenfassung:The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the implementation details of a genetic algorithm, which is used in the memetic algorithm. We then propose a constraint programming based branch-and-bound algorithm to be employed as the local search engine of the memetic algorithm. Next, we present the new memetic algorithm. We lastly explain the computational experiments carried out to evaluate the performance of three algorithms (genetic algorithm, constraint programming based branch-and-bound algorithm, and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better quality solutions and that it is very efficient.
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ISSN:1570-1166
2214-2487
1572-9214
2214-2495
DOI:10.1007/s10852-008-9101-1