Affiliation:
1. University Politehnica of Bucharest
Abstract
Flexible Manufacturing Systems-FMS is a term with various types of definitions, each of them trying to describe the complexity and the generalized features. One of these features is their complexity, along with difficulties in building models that capture the system in all its important aspects. In a heterogeneous flexible system, the scheduling events or actions could be a combinatorial problem which claims a particular solution. Manufacturing scheduling process, in special for FMS, is a very difficult scheduling problem, because involves all the aspects of the processes: order, resources, transportation system i.e. automated vehicle guided, perturbation factors such as breakdowns of machine, etc. Typically, the scheduling problem is a NP-hard problem modeled in mathematical form. If we simulate n jobs or orders which have to be assigned to the m machines or resources, we will observe that the mathematical solution is a huge number that means (n!)m possibilities of solutions. The challenge of researchers is to solve this equation in a reasonable time with an optimal solution, and of course with minimal resources. Those scientists applied many solutions which became Operational Research-OR or Combinatorial Optimization-CO areas using a various methods: Local Search-LS, Artificial Intelligence-AI, heuristic method, priority rules, memetic or hybrid techniques which combine this techniques.
Publisher
Trans Tech Publications, Ltd.
Reference58 articles.
1. L. Davis, Job shop scheduling with genetic algorithms, Proceedings of the International Conference on Genetic Algorithms and their Applications, San Mateo, Morgan Kaufmann, pp.136-149, (1985).
2. J.H. Holland, Adaptive algorithms for discovering and using general patterns in growing knowledge bases, International Journal of Policy Analysis and Information systems, 4(3), pp.245-268, (1980).
3. R. Nakano, T. Yamada, Conventional genetic algorithm for job shop problem, Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, California, pp.477-479, (1991).
4. H. Fang, P. Ross, D. Corne, A promising genetic algorithm approach to job shop scheduling, rescheduling, and open-shop scheduling problems, Proceedings of the 5th International Conference on Genetic Algorithms, Urbana-Champaign, Illinois, pp.375-382, (1993).
5. R.W. Cheng, M. Gen, Y. Tsujimura, "A tutorial survey of job shop scheduling problems using genetic algorithms: Part I: Representation, International Journal of Computers and Industrial Engineering 30(4), pp.983-997, (1996).