Journal of Intelligent Manufacturing

, Volume 12, Issue 3, pp 281–293 | Cite as

Dynamic scheduling of manufacturing job shops using genetic algorithms

  • George Chryssolouris
  • Velusamy Subramaniam
Article

Abstract

Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date.

A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.

Genetic algorithms scheduling manufacturing job shop 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aarts, E. H. L., Van Laarhoven, P. J. M., Lenstra, J. K. and Ulder, N. L. (1994) A computational study of local search algorithms for job shop scheduling. ORSA Journal of Computing, 6, 118-125.Google Scholar
  2. Anderson, E. J. and Nyirenda, J. C. (1990) Two new rules to minimize tardiness in a job-shop. International Journal of Production Research, 28, 2277-2292.Google Scholar
  3. Bagchi, S., Uckun, S., Miyabe, Y. and Kawamura, K. (1991) Exploring problem-specific recombination operators for job shop scheduling, in the Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 10-17.Google Scholar
  4. Booker, L. (1987) Genetic Algorithms and Simulated Annealing, L. Davis (ed.), Morgan Kaufman Publishers, pp. 61-73.Google Scholar
  5. Chang, Y. L., Matsuo, H. and Sullivan, R. S. (1989) Bottleneck based beam search for job scheduling in a flexible manufacturing system. International Journal of Production Research, 27, 1949-1961.Google Scholar
  6. Chryssolouris, G., (1992) Manufacturing Systems: Theory and Practice, Springer Verlag, New York.Google Scholar
  7. Cleveland, G. A. and Smith, S. F. (1989) Using genetic algorithms to schedule flow shop releases, in the Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 160-169.Google Scholar
  8. Custodio, L. M. M., Sentieiro, J. J. S. and Bispo, C. F. G. (1994) Production planning and scheduling using a fuzzy decision system. IEEE Transactions on Robotics and Automation, 10, 160-168.Google Scholar
  9. Day, J. E. and Hottenstein, M. P. (1970) Review of sequencing research. Naval Research Logistics Quarterly, 17, 11-39.Google Scholar
  10. Enns, S. T. (1993) Job shop flowtime prediction and tardiness control using queueing analysis. International Journal of Production Research, 31, 2045-2057.Google Scholar
  11. Eshelman, L. J. and Schaffer, J. D. (1991) Preventing premature convergence in genetic algorithms by preventing incest, in the Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 115-122.Google Scholar
  12. Filipic, B. (1992) Enhancing genetic search to schedule a production unit, in the Tenth European Conference on Artificial Intelligence, B. Neumann (ed.), John Wiley & Sons.Google Scholar
  13. Foo, S. Y., Takefuji Y. and Szu, H. (1994) Job-shop scheduling based on modified Tank-Hopfield linear programming networks. Engineering Applications of Artificial Intelligence, 7, 321-327.Google Scholar
  14. Gershwin, S. B. (1994) Manufacturing Systems Engineering, Prentice Hall, New Jersey.Google Scholar
  15. Grabot, B. and Geneste, L. (1994) Dispatching rules in scheduling: A fuzzy approach. International Journal of Production Research, 32, 903-915.Google Scholar
  16. He, Z., Yang, T. and Deal, D. E. (1993) Multiple-pass heuristic rule for job scheduling with due dates. International Journal of Production Research, 31, 2677-2692.Google Scholar
  17. Hoitomt, D. J., Luh, P. B. and Pattipati, K. R. (1993) A practical approach to job shop scheduling problems. IEEE Transactions on Robotics and Automation, 9, 1-13.Google Scholar
  18. Husbands, P. and Mill, F. (1991) Simulated co-evolution as the mechanism for emergent planning and scheduling, in the Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 264-270.Google Scholar
  19. Itoh, K., Huang, D. and Enkawa, T. (1993) Twofold look-ahead search for multi-criterion job shop scheduling. International Journal of Production Research, 31, 2215-2234.Google Scholar
  20. Kanet, J. J. and Sridharan, V. (1991) ProGenitor: A genetic algorithm for production scheduling. Working paper, Clemson University.Google Scholar
  21. Kannan, V. R. and Ghosh, S. (1993) Evaluation of the interaction between dispatching rules and truncation procedures in job-shop scheduling. International Journal of Production Research, 31, 1637-1654.Google Scholar
  22. Law, A. M. and Kelton, W. D. (1991) Simulation Modelling and Analysis, McGraw Hill, New York.Google Scholar
  23. Leon, V. J., Wu S. D. and Storer, R. H. (1994) A game-theoretic control approach for job shops in the presence of disruptions. International Journal of Production Research, 32, 1451-1476.Google Scholar
  24. Li, R. K., Shyu, Y. T. and Adiga, S. (1993) A heuristic rescheduling algorithm for computer-based production scheduling systems. International Journal of Production Research, 31, 1815-1826.Google Scholar
  25. Luh, P. B. and Hoitomt, D. J. (1993) Scheduling of manufacturing systems using the lagrangian relaxation technique. IEEE Transactions on Automatic Control, 38, 1066-1079.Google Scholar
  26. Mattfeld, D. C., Kopfer, H. and Bierwirth, C. (1994) Control of parallel population dynamics by social-like behaviour of GA-individuals, in the Proceedings of the Third Conference on Parallel Problem Solving from Nature, Jerusalem, pp. 16-25.Google Scholar
  27. Nakano, R. and Yamada, T. (1991) Conventional genetic algorithm for job shop problems, in the Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 474-479.Google Scholar
  28. Nasr, N. and Elsayed, E. A. (1990) Job shop scheduling with alternative machines. International Journal of Production Research, 28, 1595-1609.Google Scholar
  29. Ramasesh, R. (1990) Dynamic job shop scheduling: a survey of simulation research. OMEGA: International Journal of Management Science, 18, 43-57.Google Scholar
  30. Reeves, C. and Karatza, H. (1993) Dynamic sequencing of a multi-processor system: a genetic algorithm approach. Artificial Neural Nets and Genetic Algorithms, R. F. Albrecht, C. R. Reeves and N. C. Steele (eds.), in the Proceedings of the International Conference in Innsbruck, Austria, pp. 491-495.Google Scholar
  31. Subramaniam, V. (1995) Scheduling of manufacturing systems based on extreme value theory and genetic algorithms, PhD thesis, Department of Mechanical Engineering, Massachusetts Institute of Technology.Google Scholar
  32. Syswerda, G. and Palmucci, J. (1991) The application of genetic algorithms to resource scheduling, in the Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 502-508.Google Scholar
  33. Taillard, E. D. (1994) Parallel taboo search techniques for the job shop scheduling problem. ORSA Journal of Computing, 6(2), 108-117.Google Scholar
  34. Tamaki, H. and Nishikawa, Y. (1992) A parallel genetic algorithm based on a neighbourhood model and its application to job shop scheduling, in the Proceedings of the Second Conference on Parallel Problem Solving from Nature, Brussels, pp. 573-582.Google Scholar
  35. Thierens, D. and Goldberg, D. (1994) Convergence models of genetic algorithm selection schemes, in the Proceedings of the Third Conference on Parallel Problem Solving from Nature, Jerusalem, pp. 119-129.Google Scholar
  36. Turksen, I. B., Yurtsever, T. and Demirli, K. (1993) Fuzzy expert system shell for scheduling, in the Proceedings of the SPIE The International Society for Optical Engineering, 2061, pp. 308-319.Google Scholar
  37. Uckun, S., Bagchi, S., Kawamura, K. and Miyabe, Y. (1993) Managing genetic search in job shop scheduling. IEEE Expert, pp. 15-24.Google Scholar
  38. Van Ryzin, G. J., Lou, S. X. and Gershwin, S. B. (1991) Scheduling job shops with delays. International Journal of Production Research, 29, 1407-1422.Google Scholar
  39. Vancheeswaran, R. and Townsend, M. A. (1993) A two-stage heuristic procedure for scheduling job shops. Journal of Manufacturing Systems, 12, 315-325.Google Scholar
  40. Whitley, D., Starkweather T. and Fuquay, D. (1989) Scheduling problems and traveling salesmen: The genetic edge recombination operator, in the Proceedings of the Third International Conference on Genetic Algorithms, pp. 133-140.Google Scholar
  41. Willems, T. M. and Rooda, J. E. (1994) Neural networks for job-shop scheduling. Control Engineering Practice, 2, 31-39.Google Scholar
  42. Yamada, T. and Nakano, R. (1992) A genetic algorithm applicable to large-scale job-shop problems, in the Proceedings of the Second Conference on Parallel Problem Solving from Nature, Brussels, pp. 281-290.Google Scholar
  43. Zeestraten, M. J. (1990) The look ahead dispatching procedure. International Journal of Production Research, 28, 369-384.Google Scholar
  44. Zhou, D. N., Cherkassky, V., Baldwin, T. R. and Olson, D. E. (1991) A neural network approach to job-shop scheduling. IEEE Transactions on Neural Networks, 2, 175-179.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • George Chryssolouris
    • 1
  • Velusamy Subramaniam
    • 2
  1. 1.Laboratory for Manufacturing Systems and Automation, Department of Mechanical EngineeringUniversity of PatrasPatrasGreece
  2. 2.Department of Mechanical and Production EngineeringNational University of SingaporeSingaporeRepublic of Singapore

Personalised recommendations