Hybrid genetic algorithms for a multiple-objective scheduling problem

Journal of Intelligent Manufacturing

Volumn 9, Issue 4, 1998, Pages 361-367

  Cavalieri, Sergio ^a   Gaiardelli, Paolo ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Adaptive genetic algorithms; Dynamic population size; Flow shop; Simulation

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTER SIMULATION; DISCRETE TIME CONTROL SYSTEMS; GENETIC ALGORITHMS; MATHEMATICAL MODELS; SCHEDULING; ADAPTIVE SYSTEMS; NEURAL NETWORKS; POPULATION STATISTICS; ROBUSTNESS (CONTROL SYSTEMS);

MULTIPLE-OBJECTIVE SCHEDULING PROBLEMS;

PRODUCTION CONTROL; SCHEDULING;

ADAPTIVE GENETIC ALGORITHMS; DYNAMIC POPULATION SIZE; FLOW SHOP; LOOK-AHEAD ALGORITHMS;

EID: 0032131027     PISSN: 09565515     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008935027685     Document Type: Article

References (13)

1. Cavalieri, S. and Taisch, M. (1996) Neural networks in hybrid intelligent manufacturing systems, in Proceedings of the sixth IFIP International Conference on Advances in Production Management Systems, Okino, N. et al. (eds), 4-6 November, Kyoto, The International Federation of Information Processing, pp. 591-596.
2. Dagli, C. H. and Lee, H. C. (1995) A parallel genetic neuro scheduler for job-shop scheduling, in Proceedings of Thirteenth International Conference on Production Research, Dar-EL, E. M. et al. (eds), 6-10 August, Jerusalem, Freund Publishing House, London, pp. 602-604.
3. Dagli, C. H. and Sittisathanchai, S. (1993) Genetic neuro scheduler for job-shop scheduling. Computers and Industrial Engineering, 25, 267-270.
4. De Jong, K. A. (1975) Analysis of the behaviour of a class of genetic adaptive systems. PhD Dissertation, University of Michigan.
5. Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, pp. 412.
6. Jones, A. T. and Rabelo, L. (1993) Real decision making using neural nets, simulation and genetic algorithms. International Journal of Flexible Automation and Integrated Manufacturing, 1, 119-131.
7. Khuri, A. I. and Cornell, J. A. (1987) Response Surfaces: Design and Analyses, Marcel Dekker, New York.
8. Michalewicz, Z. (1994) GA + Data Structures = Evolution Programs, Springer-Verlag, Berlin.
9. Nakano, R. and Yamada, T. (1991) Conventional genetic algorithm for job-shop problem, in Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, Morgan Kaufmann, San Mateo, CA.
10. Reeves, C. R. (1995) A genetic algorithm for flow-shop sequencing. Computers Operations Research, 22, 5-13.
11. Sangalli, N., Semeraro, Q. and Tolio, T. (1995) Performance of a GA in the solution of permutation flow-shop problem, in Proceedings of the International Conference on Neural Networks and GAs, Ales, France.
12. Suresh, G., Vinod, V. V. and Sahu, S. (1996) A genetic algorithm for assembly line balancing. Production, Planning and Control, 7, 38-46.
13. Syswerda, G. (1991) Schedule optimization using genetic algorithms, in Handbook of Genetic Algorithms, Davis, L. (ed.), VNR Computer Library, New York, pp. 332-349.