Use of rules and preferences for schedule builders in genetic algorithms for production scheduling
Genetic algorithms (GAs) for problems such as the optimisation of production schedules require large amounts of complex accurate problem information to be included accurately, if optimisation is to be effective. One method of including problem information is the use of an encoding stage, such as a schedule builder, to supplement basic information contained within the chromosomes with data relevant to the manufacturing environment.
The problems of such a representation are explored, when modelling factory decisions , including the use of heuristic rules and preferences. Five schedule builder methods are implemented in the context of a real-life manufacturing example, to compare their effectiveness in improving the genetic algorithm optimisation performance.
KeywordsGenetic Algorithm Schedule Problem Production Schedule Manufacturing Environment Problem Information
Unable to display preview. Download preview PDF.
- Bagchi, S., Uckan, S., Miyabe, Y., Kawamura, K., 1991. Exploring Problem-Specific Recombination Operators for Job-Shop Scheduling, Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann PublishersGoogle Scholar
- Davis, L., 1985. Job Shop Scheduling with Genetic Algorithms, Proceedings of an International Conference on Genetic Algorithms, 1985.Google Scholar
- Davis, L., 1989. Adaptive Operator Probabilities in Genetic Algorithms, Proceedings of the Third International Conference on Genetic Algorithms and their Applications, 1989, pp. 60–69.Google Scholar
- Dorndorf and Pesch, 1995. Evolution Based Learning in a Job Shop Scheduling Environment, Computers and Operations Research, Vol. 22, Issue l, pp. 25–40.Google Scholar
- Fang, H. Ross, P. and Come, D., 1994. A Promising Hybrid GA/Heuristic Approach for Open-Shop Scheduling Problem, ECAI l Ith European Conference on AI, Wiley.Google Scholar
- Fox and McMahon, 1990. Genetic Operators of Sequencing Problems, Foundations of Genetic Algorithms, ed. G. J. E. Rawlings, 284–301.Google Scholar
- Giffler B., and Thompson G. L., 1969. Algorithms for solving production scheduling problems, Operations Research 8: 487–503.Google Scholar
- Langdon, W., 1996. Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming, GP-96 Conference, John Koza (ed.), Stanford Bookstore.Google Scholar
- McIlhagga, Husbands and Ives, 1996. A Comparison of Optimisation Techniques for Integrated Manufacturing Planning and Scheduling, Proceedings of Parallel Problem Solving from Nature IV.Google Scholar
- Michalewicz, Z., 1996. Evolutionary Computation; Practical Issues, International Conference on Evolutionary Computation, ICEC `96. pp. 30–39.Google Scholar
- Shaw, K.J., and Fleming, P. J., 1996. An Initial Study of Practical Multi-Objective Production Scheduling Using Genetic Algorithms, UKACC International Conference on Control `96, pp 479–485, IEE.Google Scholar
- Shaw, R., 1996. Extending the Shelf Life of Chilled Ready Meals, Developments in Meat Packaging, ed. Taylor A. A., ECCEAMST, Utrecht, The Netherlands.Google Scholar
- Syswerda and Palmucci, 1991. The Application of Genetic Algorithms to Resource Scheduling, Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 502–508.Google Scholar
- Syswerda, G., 1991. Schedule Optimisation using Genetic Algorithms, Handbook of Genetic Algorithms, ed. Davis, L., Van Nostrand Reinhold.Google Scholar
- Uckun, Bagchi, Kawamura and Miyabe, 1993. Managing Genetic Search in Job Shop Scheduling, IEEE Expert, October 1993, pp. 15–24.Google Scholar
- Whitley, D., Starkweather, T., and Fuquay, D., 1989. Scheduling Problems and Traveling Salesman; the Genetic Edge Recombination Operator. Proceedings of the Third International Conference on Genetic Algorithms, pp 133–140.Google Scholar