Weaknesses of Ant System for the Distributed Job Shop Scheduling Problem

Imen Chaouch, Olfa Belkahla Driss, Khaled Ghedira

2017

Abstract

Globalization has opened up huge opportunities for the plant and industrial investors. The problem of single plant is now more generalised, namely, multi factory problem. This paper deals with the problem of Distributed Job shop Scheduling in multi-factories. The problem solving process consists of finding an effective way to assign jobs to factories then, to generate a good operation schedule. To make this, an Ant System algorithm is implemented. Several numerical experiments are conducted to evaluate the performance of the Ant System algorithm applied to the Distributed Job shop Scheduling, and the results show the shortcoming of the standard Ant System algorithm compared to developed algorithms in the literature.

References

  1. Adams, J., Balas, E., and Zawack, D. (1988). The shifting bottleneck procedure for job shop scheduling. Management science, 34(3):391-401.
  2. Bargaoui, H., Belkahla Driss, O., and Ghédira, K. (2016). Minimizing makespan in multi-factory flow shop problem using a chemical reaction metaheuristic. In IEEE Congress on Evolutionary Computation, At Vancouver, Canada, pages 2919-2929.
  3. Bullnheimer, B., Hartl, R. F., and Strauss, C. (1997). A new rank based version of the ant system. a computational study.
  4. Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., and Raidl, G. R. (2002). Applications of Evolutionary Computing: EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN Kinsale, Ireland, April 3-4, 2002. Proceedings, volume 2279. Springer Science & Business Media.
  5. Chung, S. H., Lau, H. C., Ho, G. T., and Ip, W. (2009). Optimization of system reliability in multi-factory production networks by maintenance approach. Expert Systems with Applications, 36(6):10188-10196.
  6. Colorni, A., Dorigo, M., Maniezzo, V., and Trubian, M. (1994). Ant system for job-shop scheduling. Belgian Journal of Operations Research, Statistics and Computer Science, 34(1):39-53.
  7. Davis, L. (1985). Job shop scheduling with genetic algorithms. In Proceedings of an international conference on genetic algorithms and their applications, volume 140. Carnegie-Mellon University Pittsburgh, PA.
  8. Dell'Amico, M. and Trubian, M. (1993). Applying tabu search to the job-shop scheduling problem. Annals of Operations Research, 41(3):231-252.
  9. Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano, Italy.
  10. Dorigo, M. and Gambardella, L. M. (1997a). Ant colonies for the travelling salesman problem. BioSystems, 43(2):73-81.
  11. Dorigo, M. and Gambardella, L. M. (1997b). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1):53-66.
  12. Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1):29-41.
  13. Fisher, H. and Thompson, G. L. (1963). Probabilistic learning combinations of local job-shop scheduling rules. Industrial scheduling, 3(2):225-251.
  14. Gambardella, L. M. and Dorigo, M. (1996). Solving symmetric and asymmetric tsps by ant colonies. In International conference on evolutionary computation, pages 622-627.
  15. Gao, J. and Chen, R. (2011). A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. International Journal of Computational Intelligence Systems, 4(4):497-508.
  16. Garey, M. R., Johnson, D. S., and Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of operations research, 1(2):117-129.
  17. Jia, H., Fuh, J. Y., Nee, A. Y., and Zhang, Y. (2002). Webbased multi-functional scheduling system for a distributed manufacturing environment. Concurrent Engineering, 10(1):27-39.
  18. Jia, H., Fuh, J. Y., Nee, A. Y., and Zhang, Y. (2007). Integration of genetic algorithm and gantt chart for job shop scheduling in distributed manufacturing systems. Computers & Industrial Engineering, 53(2):313-320.
  19. Jia, H., Nee, A. Y., Fuh, J. Y., and Zhang, Y. (2003). A modified genetic algorithm for distributed scheduling problems. Journal of Intelligent Manufacturing, 14(3- 4):351-362.
  20. Karimi, N. and Davoudpour, H. (2017). A knowledge-based approach for multi-factory production systems. Computers & Operations Research, 77:72-85.
  21. Lawrence, S. (1984). Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (supplement). Graduate School of Industrial Administration.
  22. Maniezzo, V. (1999). Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing, 11(4):358-369.
  23. Naderi, B. and Azab, A. (2014). Modeling and heuristics for scheduling of distributed job shops. Expert Systems with Applications, 41(17):7754-7763.
  24. Naderi, B. and Azab, A. (2015). An improved model and novel simulated annealing for distributed job shop problems. The International Journal of Advanced Manufacturing Technology, pages 1-11.
  25. Naderi, B. and Ruiz, R. (2010). The distributed permutation flowshop scheduling problem. Computers & Operations Research, 37(4):754-768.
  26. Stützle, T. (1998). Local search algorithms for combinatorial problems. Darmstadt University of Technology PhD Thesis, 20.
  27. Stützle, T. and Hoos, H. (1997). Max-min ant system and local search for the traveling salesman problem. In Evolutionary Computation, 1997., IEEE International Conference on, pages 309-314. IEEE.
  28. Stützle, T. and Hoos, H. (2000). Max-min ant system. Future generation computer systems, 16(8):889-914.
  29. Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2):278-285.
  30. Talbi, E.-G. (2009). Metaheuristics: from design to implementation, volume 74. John Wiley & Sons.
Download


Paper Citation


in Harvard Style

Chaouch I., Belkahla Driss O. and Ghedira K. (2017). Weaknesses of Ant System for the Distributed Job Shop Scheduling Problem . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 574-581. DOI: 10.5220/0006332405740581


in Bibtex Style

@conference{iceis17,
author={Imen Chaouch and Olfa Belkahla Driss and Khaled Ghedira},
title={Weaknesses of Ant System for the Distributed Job Shop Scheduling Problem},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={574-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006332405740581},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Weaknesses of Ant System for the Distributed Job Shop Scheduling Problem
SN - 978-989-758-247-9
AU - Chaouch I.
AU - Belkahla Driss O.
AU - Ghedira K.
PY - 2017
SP - 574
EP - 581
DO - 10.5220/0006332405740581