A genetic algorithm for the Flexible Job-shop Scheduling Problem

Authors:
F. Pezzella;G. Morganti;G. Ciaschetti
Affiliations:
Dipartimento di Ingegneria Informatica, Gestionale e dell'Automazione, Universití Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy;Dipartimento di Ingegneria Informatica, Gestionale e dell'Automazione, Universití Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy;Universití Politecnica delle Marche, Italy
Venue:
Computers and Operations Research
Year:
2008

Citing 5
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Abstract

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.