A genetic algorithm for the Flexible Job-shop Scheduling Problem

In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the inte...

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Bibliographic Details
Published in:Computers & operations research 2008-10, Vol.35 (10), p.3202-3212
Main Authors: Pezzella, F., Morganti, G., Ciaschetti, G.
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Flexible manufacturing systems
Genetic algorithms
Job shops
Job-shop Scheduling
Operational research and scientific management
Operational research. Management science
Problem solving
Production scheduling
Scheduling, sequencing
Studies
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Summary:In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are quite comparable to those obtained by the best-known algorithm, based on tabu search. These two results, together with the flexibility of genetic paradigm, prove that genetic algorithms are effective for solving FJSP.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2007.02.014