Abstract
The Flexible Job Shop problem is among the hardest scheduling problems. It is a generalization of the classical Job Shop problem in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to assign and to sequence the operations on the resources so that they are processed in the smallest time. In our previous work, we have proposed two Multi-Agent approaches based on the Tabu Search (TS) meta-heuristic. Depending on the location of the optimisation core in the system, we have distinguished between the global optimisation approach where the TS has a global view on the system and the local optimisation approach (FJS MATSLO) where the optimisation is distributed among a collection of agents, each of them has its own local view. In this paper, firstly, we propose new diversification techniques for the second approach in order to get better results and secondly, we propose a new promising approach combining the two latter ones. Experimental results are also presented in this paper in order to evaluate these new techniques.
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References
Adams J., Balas E. and Zawak D. (1988). The shifting bottleneck procedure for the job shop scheduling problem. Management Science 34(3): 391–401
Brandimarte P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research 22: 158–183
Brucker P. and Neyer J. (1998). Tabu-search for the multi-mode job-shop problem. OR Spektrum 20: 21–28
Chambers, J. B., & Barnes, J. W. (1996). Flexible job shop scheduling by tabu search. Graduate program in Operations Research and Industrial Engineering, The University of Texas at Austin, Technical Report series, ORP96-09.
Cox, J. S., & Durfee, E. H. (2003). Discovering and exploiting synergy between hierarchical planning agents. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS’03 (pp. 281–288). Melbourne, Australia 2003.
Crawford, E., & Veloso, M. (2004). Opportunities for learning in multi-agent meeting scheduling. In The Proceedings of the AAAI 2004, Symposium on Artificial Multi-Agent Learning, Washington.
Dauzerre-Peres S. and Paulli J. (1997). An integrated approach for modeling and solving the general multi-processor job shop scheduling problem using tabu search. Annals of Operations Research 70: 281–306
Ennigrou, M., & Ghédira, K. (2004). Flexible job shop scheduling with a multi-agent system and tabu search. In 17ème Conférence internationale sur les applications industrielles de l’Intelligence Artificielle et les Systèmes Experts IEA_AIE. Ottawa, Canada, 17–20 Mai 2004.
Fisher, H., & Thompson, G. L. (1963). Probabilistic learning combinations of local job-shop scheduling rules. In J. F. Muth & G. L. Thompson (Eds.), Industrial scheduling (Ch 15, pp. 225–251). Prentice Hall, Englewood Cliffs, New Jersey.
Glover F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and Operations Research 5: 533–549
Hurink E., Jurisch B. and Thole M. (1994). Tabu search for the job shop scheduling problem with multi-purpose machine. Operations Research Spektrum 15: 205–215
Jain A.S. and Meeran S. (1998). Job shop scheduling using neural networks. International Journal of Production Research 36(5): 1249–1272
Jain, A., Rangaswamy, B., & Meeran, S. (2000). Job shop neighbourhoods and move evaluation strategies. http://citeseer.ist.psu.edu/161759.html. Accessed 3 Oct 1998.
Jennings N.R. and Jackson A.J. (1995). Agent based meeting scheduling: A design and implementation. IEE Electronics Letters 31(5): 350–352
Mastrolilli M. and Gambardella L.M. (2000). Effective neighborhood functions for the flexible job shop problem. Journal of Scheduling 3(1): 3–20
Najid N.M., Dauzere-Peres S. and Zaidat A. (2002). A modified simulated annealing method for flexible job shop scheduling problem. IEEE International Conference on Systems, Man and Cybernetics 5(6–9): 6
Nuijten W. and Aarts E. (1996). A computational study of constraint satisfaction for multiple capacitated job shop scheduling. European Journal of Operations Research 90(2): 269–284
Sen S. and Durfee E.H. (1998). A formal study of distributed meeting scheduling. Group Decision and Negotiation 7: 265–289
Tanev, I. T., Yozumi, T., & Morotome, Y. (2002). Priority dispatching rules-based genetic representation for hybrid evolutionary algorithm for flexible job shop scheduling problem. Genetic and Evolutionary Computation COnference GECCO 02.
Van Brussel H., Wyns J., Valckenaers P., Bongaerts L. and Peeters P. (1998). Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry, special issue on intelligent manufacturing systems 37(3): 255–276
Walker Scott S., Brennan R. and Norrie D. (2005). Holonic job shop scheduling using a multi-agent system. IEEE Intelligent Systems 20(1): 50–57
Xu X., Guan Q., Wang W. and Shengyong C. (2005). Transient chaotic discrete neural network for flexible job-shop scheduling. International Symposium on Neural Networks 3496: 762–769
Zheng H. and Gen M. (2005). Multi-stage based genetic algorithm for flexible job shop scheduling problem. Complexity International 11: 223–232
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Ennigrou, M., Ghédira, K. New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach. Auton Agent Multi-Agent Syst 17, 270–287 (2008). https://doi.org/10.1007/s10458-008-9031-3
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DOI: https://doi.org/10.1007/s10458-008-9031-3