Dynamic scheduling for multi-site companies: A decisional approach based on reinforcement multi-agent learning

Journal of Intelligent Manufacturing

Volumn 23, Issue 6, 2012, Pages 2513-2529

  Aissani, N ^a   Bekrar, A ^b,c   Trentesaux, D ^b,c   Beldjilali, B ^a  

^a UNIVERSITY OF ORAN   (Algeria)

^b UNIV LILLE   (France)

^c LAMIH   (France)

Author keywords

Multi agent system; Multi site company; Production control; Reinforcement learning; Scheduling

Indexed keywords

ADAPTIVE SCHEDULING; DYNAMIC SCHEDULING; ENVIRONMENTAL FLUCTUATIONS; INTEGER LINEAR PROGRAMS; LEARNING CAPABILITIES; LEARNING TECHNIQUES; MULTI AGENT SYSTEM (MAS); MULTI-AGENT APPROACH; MULTI-AGENT LEARNING; MULTI-SITE; OPTIMALITY;

COMPETITION; ELECTRONIC DATA INTERCHANGE; INTEGER PROGRAMMING; INTELLIGENT AGENTS; MULTI AGENT SYSTEMS; PRODUCTION CONTROL; REINFORCEMENT LEARNING; SCHEDULING; SECURITY OF DATA;

INDUSTRY;

EID: 84870881943     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-011-0580-y     Document Type: Article

References (68)

1. Aissani, N., Trentesaux, D., & Beldjilali, B. (2008). Use of machine learning for continuous improvement of the real time manufacturing control system performances. International Journal of Industrial System Engineering, 3(4), 474-497.
2. Aissani, N., Beldjilali, B., & Trentesaux, D. (2008b). Efficient and effective reactive scheduling of manufacturing system using SARSA-multi- objective-Agents. In Proceedings of the 7th international conference MOSIM, Paris, pp. 698-707.
3. Aissani, N., Trentesaux, D., & Beldjilali, B. (2009). Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach. EAAI: Engineering Applications of Artificial Intelligence, 22, 1089-1103.
4. Ait Si Larbi, E. Y., Aissani, N., & Beldjilali, B. (2008). Un Modèle de Planification pour les Entreprises Multi Sites basé sur les Systèmes Multi Agents et les Algorithmes Génétiques. In Proceedings of the 10th maghrebian conference on information technologies, Oran, Algeria, pp. 506-511.
5. Bousbia, S., & Trentesaux, D. (2002). Self-organization in distributed manufacturing control: State-of-The-Art and future trends. IEEE International Conference on Systems, Man & Cybernetics, 5, 6.
6. Brucker, P., & Schlie, R. (1990). Job shop scheduling with multipurpose machines. Computing, 45, 369-375.
7. Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41, 157-183.
8. Chuin Lau, H., Agussurja, L., & Thangarajoo, R. (2008). Real-Time supply chain control via multi-Agent adjustable autonomy. Computers and Operations Research, 35(11), 3452-3464.
9. Conway, R. W., Maxwell, W. L., & Miller, L. W. (1967). Theory of scheduling. Reading, MA: Addison-Wesley.
10. Dabbene, F., Gay, P., Tortia, C., & Sacco, N. (2005). Optimization of fresh-food supply chains in uncertain environments: An application to the meat-refrigeration process, decision and control, European Control Conference. CDC-ECC;05. 44th IEEE Conference on Volume 12, Issue 15, pp. 2077-2082.
11. Dauzere-Peres S., & Paulli, J. (1994). Solving the general job-shop scheduling problem. Management. Report Series, vol. 182. Erasmus University Rotterdam, Rotterdam School of Management, Rotterdam.
12. Dauzère-Pérès, S., & Paulli, J. (1997). An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Annals of Operations Resarch, 70, 281-306.
13. Dongbing, G., & Yang, E. (2007). Fuzzy policy reinforcement learning in cooperative multi-robot systems. Journal of Intelligent and Robotic Systems, 48(1), 7-22.
14. Fattahi, P., Saidi Mehrabad, M., & Jolai, F. (2007). Mathematical modelling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent and Manufacturing, 18, 331-342.
15. Ferber, J., & Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-Agent systems. In Proceedings ICMAS'98, pp. 128-135.
16. Fontan, G., Merce, C., & Erschler, J. (2001). La planification des flux de production, Performance industrielle et gestion des flux, Hermes Lavoisier, Traité IC2 Information-Commande-Communication, 2001, Chap 3, pp. 69-112.
17. Galliano, D., & Soulie, N. (2007). Organisational and spatial determinants of the multiunit firm: Evidence from the French indus, Cahiers du GRES, No 17.
18. Gao, J., Gen, M., Sun, L., & Zhao, X. (2007). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers and Industrial Engineering, 53, 149-162.
19. Goldberg, D. E., & Lingle, R. (1985). Alleles, loci and the traveling salesman problem. In Proceedings of the first international conference on Genetic Algorithms, pp. 10-19.
20. Haruno, M., & Kawato, M. (2006). Heterarchical reinforcementlearning model for integration of multiple cortico-striatal loops: FMRI examination in stimulus-Action-reward association learning. Neural Networks, 19(Special Issue), 1242-1254.
21. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, IL: University of Michigan Press.
22. Hong, G., Chuan, L., Zhang, L., & Xianming, Z. (2007) Study on supply chain optimization scheduling of networked manufacturing, ACOS'07: In Proceedings of WSEAS: 6th international conference on applied computer science, Vol 6.
23. IBM. (2010). IBM ILOG CPLEX optimizer, high performance mathematical optimization engines. http://www-01.ibm.com/software/integration/optimization/ cplex-optimizer/.
24. James, T. L., Brown, E. C., & Keeling, K. B. (2007). A hybrid grouping genetic algorithm for the cell formation problem. Computers and operations research. No 34, pp. 2059-2079.
25. Kacem, I., Hammadi, S., & Borne, P. (2002). Approach by localization and multiobjective evolutionary optimization for flexible job shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C, 32(1), 408-419.
26. Katalinic, B., & Kordic, V. (2004). Bionic assembly system: Concept, structure and function. In Proceedings of the 5th IDMME, Bath, UK.
27. Lee, H.L., & Rosenblatt, J. (1986). A generalized quantity discount pricing model to increase supplier's profits. Management Science, 33(9), 1167-1185.
28. Leitao, P., & Restivo, F. (2008). A holonic approach to dynamic manufacturing scheduling. Robotics and Computer-Integrated Manufacturing, 24, 625-634.
29. Marquès, G., Lamothe, J., Thierry, C., & Gourc, D. (2009). A supply chain performance analysis of a pull inspired supply strategy faced to demande uncertainties. Journal of Intel Manufacturing, 20(6). doi:10.1007/s10845-009-0337-z.
30. Mastrolilli, M., & Gambardella, L. M. (2000). Effective neighbourhood functions for the flexible job shop problem. Journal of Scheduling, 3, 3-20.
31. Mati, Y., Rezg, N., & Xie, X.L. (2001). Geometric approach and taboo search for scheduling flexible manufacturing systems. IEEE Transactions on Robotics and Automation, 17, 805-818.
32. Mati, Y., Lahlou, C., & Dauzère-Pérès, S.(2010). Modelling and solving a practical flexible job-shop scheduling problem with blocking constraints. International Journal of Production Research, 1366-588X, First published on 23 Sep. 2010.
33. McCulloch, W. S. (1945). A heterarchy of values determined by the topology of nervous nets. Bulletin of Mathematical Biology, 7, 89-93.
34. Miles, R. E., & Snow, C. C. (1992). Managing 21st century network organisations. Organizational dynamics, winter session.
35. Mintzberg, H. (1980). Structure in 5's: A synthesis of the research. Organization Design Management Science, 26(3).
36. Monahan, J. P. (1984). A quantity pricing model to increase vendor profits. Management Science, 30(6), 720-726.
37. Monostori, L., Csáji, B. Cs., & Kádár, B. (2004). Adaptation and learning in distributed production control. CIRP Annals-Manufacturing Technology, 53(1), 349-352.
38. Mourtzis, D., Papakostas, N., Makris, S., Xanthakis, V., & Chryssolouris, G. (2008). Supply chain modeling and control for producing highly customized products. General assembly of CIRP No58, Manchester, UK (24/08/2008), Vol. 57, No 1, p. 588.
39. Narasimhan, R., & Mahapatra, S. (2004). Decision models in global supply chain management. Industrial Marketing Management, 33, 21-27.
40. Ouhimmou, M., D'Amours, S., Beauregard, R., Ait-Kadi, D., & Singh Chauhan, S. (2008). Furniture supply chain tactical planning optimization using a time decomposition approach. European Journal of Operational Research, 189(3), 952-970.
41. Ounnar, F., & Pujo, P. (2009). Pull control for job shop: Holonic manufacturing system approach using multicriteria decision-making. Journal of Intelligent Manufacturing. doi:10.1007/s10845-009-0288-4.
42. Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Computers & Operations Research, 35(10), Oct. 2008.
43. Prabhu, V. V. (2003). Stability and fault adaptation in distributed control of heterarchical manufacturing job shops. IEEE Transactions on Robotics and Automation, 19(1), 142-147.
44. Rummery, G., & Niranjan, M. (1994). On-line q-learning using connectionist systems. Technical Report CUED/F-INFENG/TR 166. Cambridge University, Engineering Department.
45. Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach, the intelligent agent book. Prentice Hall Series in Artificial Intelligence.
46. Sallez, Y., Berger, T., & Trentesaux, D. (2009). A stigmergic approach for dynamic routing of active products in FMS. Computers in Industry, 60(3), 204-216.
47. Sauer, J., Freese, T., & Teschke, T. (2000). Towards agent-based multisite scheduling. ECAI 2000 workshop on new results in planning, scheduling and design, pp. 123-130.
48. Silva, N., Sousa, P., & Ramos, C. (1998). A holonic manufacturing system implementation. Advanced Summer Institute(ASI'98). Bremen, Germany; 14-17 June 1998.
49. Swaminathan, J., Smith, S., & Sadeh-Koniecpol, N. (1997). Modeling supply chain dynamics: A multiagent approach. Decision Sciences.
50. Takadama, K., & Fujita, H. (2004). Lessons learned from comparison between Q-learning and sarsa agents in bargaining game. In North American association for computational social and organizational science (NAACSOS 2004), June 27-29, Pittsburgh, PA.
51. Tang, C. S. (1990). The impact of uncertainty on a production line. Management Science, 36(12), 1518-1531.
52. Tarantilis, C. D. (2008). Topics in real-Time supply chain management. Computers & Operations Research, 35(11), 3393-3396.
53. TehraniNik Nejad, H., Sugimura, N., Iwamura, K., & Tanimizu, Y. (2008). Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system. Journal of Intelligent Manufacturing, 21(4), 487-499.
54. Thierry, C. (2003). gestion de chaînes logistiques, modèles et mise en oeuvre pour l'aide à la décision à moyen terme. HDR thesis, Toulouse 2 university.
55. Trentesaux, D., Pesin, P.,&Tahon, C. (2000).Distributed artificial intelligence for FMS scheduling, control and design support. Journal of Intelligent Manufacturing, 11, 573-589.
56. Trentesaux, D. (2009). Distributed control of production systems. Engineering Applications of Artificial Intelligence, 22(7), 971-978.
57. Tsai, J.-F. (2007). An optimization approach for supply chain management models with quantity discount policy. European Jounal of Operational Research, 177(2), 982-994.
58. Vaario, J., & Ueda, K. (1998). An emergent modelling method for dynamic scheduling. Journal of IntelligentManufacturing, 9, 129-140.
59. Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., & Peeters, L. (1998). Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry, 37(3), 255-274.
60. Vilcot, G. (2007). Algorithmes approchés pour des problèmes d'ordonnancement multicritère de type job shop flexible et job shop multiressource, PhD thesis, University of François-Rabelais, Tours, France.
61. Voss, S., & Woodruff, D. L. (2006). Introduction to computational optimization models for production planning in supply chain. Berlin: Springer.
62. Watkins, C. J. C. H. (1989). Learning from delayed rewards, PhD thesis, Cambridge University, Cambridge, England.
63. Weng, Z. K., & Wong, R. T. (1993). General models for the supplier's all-unit quantity discount policy. Naval Research Logistics, 40(6), 971-991.
64. Yang, P. C. (2004). Pricing strategy for deteriorating items using quantity discount when demand is price sensitive. European Journal of Operational Research, 157, 389-397.
65. Zambrano, G., Aissani, N., Pach, C., Berger, T., & Trentesaux, D. (2011). An approach for temporal myopia reduction in heterarchical control architectures. In Proceedings of 20th IEEE inter symposium on industrial electronics, 27-30 June 2011.
66. Zbib, N., Pach, C., Sallez, Y., & Trentesaux, D. (2010). Heterarchical production control in manufacturing systems using the potential fields concept. Journal of IntelligentManufacturing. doi:10.1007/s10845-010- 0467-3.
67. Zhang, H., & Gen, M. (2005). Multistage-based genetic algorithm for flexible job-shop scheduling problem. Journal of Complexity International, 11, 223-232.
68. Zobolas, G. I., Tarantilis, C. D., & Ioannou, G. (2008). Exact, Heuristic and Meta-heuristic Algorithms for Solving Shop Scheduling Problems, Studies in Computational Intelligence (SCI). (pp. 1-40). Berlin: Springer.