Efficient generation of near optimal initial populations to enhance genetic algorithms for Job-Shop Scheduling

Information Technology and Control

Volumn 39, Issue 1, 2010, Pages 32-37

  Kuczapski, Artur M ^a   Micea, Mihai V ^a   Maniu, Laurentiu A ^b   Cretu, Vladimir I ^a  

^a POLITEHNICA UNIVERSITY OF TIMISOARA   (Romania)

^b Advanced clean production Information Technology (acp IT)   (Romania)

Author keywords

Chromosomes; Genetic algorithms; Initial populations; Job Shop Scheduling

Indexed keywords

EID: 77952709893     PISSN: 1392124X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. st Ed. Springer New York, 2006.
2. K. Kemppainen. Priority Scheduling Revisited: Dominant Rules, Open Protocols, and Integrated Order Management. Helsinki School of Economics, 2005.
3. A. S. Jain, S. Meeran. Deterministic job-shop scheduling: past, present and future. European Journal of Operational Research, 113 (2), 1999, 390-434.
4. J. Käschel, T. Teich, G. Köbernik, B. Meier. Algorithms for the Job Shop Scheduling Problem: A Comparison of Different Methods. European Symposium on Intelligent Techniques, Greece, Jun. 1999.
5. C. Bierwirth. A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms. OR Spektrum, 17, 1995, 87-92.
6. S.-C. Lin, E. D. Goodman, W. F. Punch, III. A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problems. Proceedings of the Seventh International Conference on Genetic Algorithms, 1997, 481-488.
7. J. Garen. Multiobjective Job-Shop Scheduling With Genetic Algorithms Using a New Representation and Standard Uniform Crossover. Workshop on Multiple Objective Metaheuristics, Paris, Nov. 2002.
8. P. Larranaga, C. M. H. Kuijpers, R. H. Murga, I. Inza, S. Dizdarevic. Genetic Algorithms for the traveling Salesman Problem: A Review of Representations and Operators. Artificial Intelligence Review, 13, 1999, 129-170.
9. N. B. Ho, J. C. Tay. Evolving Dispatching Rules for solving the Flexible Job-Shop Problem. IEEE Congress on Evolutionary Computation, 3, 2005, 2848-2855.
10. st Ed., Morgan Kaufmann San Francisco, 1997.
11. H. Fisher, G. L. Thompson. Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules. Industrial Scheduling, J. F. Muth and G. L. Thompson (eds.), Prentice-Hall Englewood Cliffs, NJ, 1963, 225-251.
12. S. Lawrence. Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques. Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, Pennsylvania, 1984.
13. V. T'kindt, J.-C. Billaut. Multicriteria Scheduling. European Journal of Operational Research, 167 (3), Elsevier B. V., Dec. 2006, 589-591.
14. J. E. Baker. Reducing Bias and Inefficiency in the Selection Algorithm. Proceedings of the Second International Conference on Genetic Algorithms and their Application (Hillsdale), 1987, 14-21.
15. B. Giffler, G. L. Thompson. Algorithms for Solving Production-Scheduling Problems. Operation Research, 8 (4), 1960, 487-503.
16. T. Yamada, R. Nakano. Genetic Algorithms for Job-Shop Scheduling Problems. Proceedings of Modern Heuristic for Decision Support, UNICOM Seminar, London, 1997, 67-81.