Abstract
General job shop scheduling and rescheduling with alternative route choices for an FMS environment is addressed in this paper. A genetic algorithm is proposed to derive an optimal combination of priority dispatching rules “pdrs” (independentpdrs one each for one Work Cell “WC”), to resolve the conflict among the contending jobs in the Giffler and Thompson “GT” procedure. The performance is compared with regard to makes-pan criteria and computational time. The optimal WCwise-pdr is proved to be efficient in providing optimal solutions in a reasonable computational time. Also, the proposed GA based heuristic method is extended to revise schedules on the arrival of new jobs, and on the failure of equipment to address the dynamic operation mode of flexible manufacturing systems. An iterative search technique is proposed to find the best route choice for all operations to provide a feasible and optimal solution. The applicability and usefulness of the proposed methodology for the operation and control of FMS in real-time are illustrated with examples. The scope of the genetic search process and future research directions are discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
D. A. Taglia and M. Santochi, “A software architecture for FMS simulation and control”, Annals CIRP, 42(1), pp. 489–492, 1993.
D. C. Li and I.-S. She, “Using unsupervised learning technologies to induce scheduling knowledge for FMS”, International Journal of Production Research, 32(9), pp. 2187–2199, 1995.
P. Mellor, “A review of job shop scheduling”, Operation Research Quarterly, 17(2), pp. 161–171, 1966.
P. Brucker, Scheduling Algorithms, Springer-Verlag, Berlin-Heidelberg, 1st edn, 1995.
J. Hutchison, “Current and future issues concerning FMS scheduling”, International Journal of Management Sciences, 19(6), pp. 529–537, 1991.
M. P. Fanti, E. L. O. Valvo, M. Piecentini and G. Piscitellil, “Real-time routeing selection in flexible manufacturing systems”, Proceedings of IMTDR Conference, New Delhi, pp. 727–732, 1993.
N. Nasr and E. A. Elsayed, “Job shop scheduling with alternative machines”, International Journal Production Research, 28(9), pp. 1595–1609, 1990.
M. A. Nascimento, “Giffler and Thompson algorithm for job shop scheduling is still good for flexible manufacturing systems”, Journal of the Operational Research Society, 44(5), pp. 521–524, 1993.
B. Giffler and G. L. Thompson, “Algorithms for solving production scheduling problems”, International Journal of Operations Research, 8, pp. 487–503, 1960.
M. H. Kim and Y.-D. Kim, “Simulation based real-time scheduling in FMS”, Journal of Manufacturing System, 13(2), pp. 85–93, 1994.
H. H. Greenberg, “A branch and bound solution to the general scheduling problem”, International Journal of Operation Research, 16, pp. 353–361, 1968.
N. Jawahar, P. Aravindan, S. G. Ponnambalam and A. Arvindkarthi-keyan, “Branch bound technique in combination with priority dispatching rules for scheduling FMS”, Proceedings of the International Conference on CAD/CAM, Automation, Robotics and Factories of the future (INCARF'96), New Delhi, pp. 143–150, 1996.
K. R. Baker, “Introduction to Sequencing and Scheduling”, Wiley, New York, 1974.
Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs”, Springer-Verlag, Berlin-Heidelberg, USA, 1992.
H. Kopfer and D. C. Mattfield, “A hybrid search algorithm for the job shop problem”, Proceedings of the First International Conference on Operations and Quantitative Management, Vol. 2, pp. 498–505, 1997.
T. Masters, “Practical Neural Network Recipes in C++”, Academic Press, USA, 1993.
J. Schultz and P. Mertens, “A comparison between an expert system, a GA and priority for production scheduling”, Proceedings of the First International Conference on Operations and Quantitative Management”, vol. 2, pp. 505–513, 1997.
J. E. Biegel and J. J. Davern, “Genetic algorithms and job shop scheduling”, International Journal of Computers and Industrial Engineering, 19(1–4), pp. 81–90, 1990.
U. Dorndorf and E. Pesche, “Combining genetic and local search for solving the job shop scheduling problem”, APMOD93 Proceedings, Budapest, pp. 142–149, 1993.
R. E. Billo, B. Bidanda and D. Tate, “A genetic algorithm formulation of the cell formation problem”, Proceedings of the 16th International Conference on Computers and Industrial Engineering, 7–9 March, pp. 341–344, 1994.
E. h. H.-P. Weindahl and R. Garlichs, “Decentral production scheduling of assembly systems with genetic algorithm”, Annals CIRP, 43(1), pp. 389–395, 1994.
U. Dorndorf and E. Pesch, “Evolution based learning in a job shop environment”, Computers and Operations Research, 22, pp. 25–40, 1995.
J. Sridhar and C. Rajendran, “Scheduling in flow shop and cellular manufacturing with multiple objectives — a genetic algorithmic approach”, International Journal of Production Planning and Control, 7(4), pp. 374–382, 1996.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jawahar, N., Aravindan, P. & Ponnambalam, S.G. A genetic algorithm for scheduling flexible manufacturing systems. Int J Adv Manuf Technol 14, 588–607 (1998). https://doi.org/10.1007/BF01301703
Issue Date:
DOI: https://doi.org/10.1007/BF01301703