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Flexible Job-Shop Scheduling Problems: Formulation, Lower Bounds, Encoding and Controlled Evolutionary Approach
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Author(s): Imed Kacem (Laboratoire d’Automatique et Informatique de Lille, France), Slim Hammadi (Laboratoire d’Automatique et Informatique de Lille, France)and Pierre Borne (Laboratoire d’Automatique et Informatique de Lille, France)
Copyright: 2003
Pages: 30
Source title:
Computational Intelligence in Control
Source Author(s)/Editor(s): Masoud Mohammadian (University of Canberra, Australia), Rahul A. Sarker (University of New South Wales, Australia)and Xin Yao (The University of Birmingham, UK)
DOI: 10.4018/978-1-59140-037-0.ch014
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Abstract
The Job-shop Scheduling Problem (JSP) is one of hardest problems; it is classified NP-complete (Carlier & Chretienne, 1988; Garey & Johnson, 1979). In the most part of cases, the combination of goals and resources can exponentially increase the problem complexity, because we have a very large search space and precedence constraints between tasks. Exact methods such as dynamic programming and branch and bound take considerable computing time (Carlier, 1989; Djerid & Portmann, 1996). Front to this difficulty, meta-heuristic techniques such as evolutionary algorithms can be used to find a good solution. The literature shows that they could be successfully used for combinatorial optimization such as wire routing, transportation problems, scheduling problems, etc. (Banzhaf, Nordin, Keller & Francone, 1998; Dasgupta & Michalewicz, 1997).
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