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Extraction of Priority Rules for Boolean Induction in Distributed Manufacturing Control

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Service Orientation in Holonic and Multi-Agent Manufacturing and Robotics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 544))

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Abstract

In reactive manufacturing control, the allocation of resources for tasks is achieved in real time. When a resource becomes available it chooses one of the tasks in its queue. This choice is made according to priority rules which are designed to optimize costs, time, etc. In this paper, the aim is to exploit a Job Shop scheduling log and simulations in order to extract knowledge enabling one to create rules for the selection of priority rules. These rules are implemented in a CASI cellular automaton. Firstly, symbolic modelling of the scheduling process is exploited to generate a decision tree from the log and simulations. Secondly, decision rules are extracted to select priority rules for execution in a specific situation. Finally, the rules are integrated in CASI which implements the decisional module of agents in a distributed manufacturing control system.

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References

  1. Aissani, N.D., Trentesaux, D., Beldjilali, B.: Efficient and effective reactive scheduling of manufacturing system using SARSA-Multi-objective-agents. In: MOSIM 2008: 7th Int. Conf. on Modelling & Simulation, pp. 698–707. Lavoisier (2008)

    Google Scholar 

  2. Aissani, N.D., Trenteseaux, D., Beldjilali, B.: Dynamic Scheduling of Maintenance Tasks in the Petroleum Industry: a Reinforcement Approach. EAAI: International Scientific Journal Engineering Applications of Artificial Intelligence 22, 1089–1103 (2009) ISSN: 0952-1976

    Google Scholar 

  3. Aissani, N.D., Trenteseaux, D., Beldjilali, B.: Dynamic scheduling for multi-site com-panies: a decisional approach based on reinforcement multi-agent learning. JIM: Journal of Intell. Manufacturing 22, 1089–1103 (2011), doi:10.1007/s10845-011-0580-y

    Google Scholar 

  4. Amrani, F., Bouamrane, K.: Towards a cellular indexing in a case based reasoning approach: Application to an urban transportation system regulation. In: Proceedings of the 2010 Conf. on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade, pp. 321–332. IOS Press, Amsterdam (2010)

    Google Scholar 

  5. Atmani, B., Beldjilali, B.: Knowledge Discovery in Database: Induction Graph and Cellular Automaton. Computing and Informatics, Journal 26(2), 171–197 (2007)

    MATH  Google Scholar 

  6. Bousbia, S., Trentesaux, D.: Self-organization in distributed manufacturing control: State-of-the-art and future trends. In: IEEE International Conference on Systems, Man & Cybernetics, vol. 5, p. 6 (2002)

    Google Scholar 

  7. Chen, C.C., Yih, Y.: Identifying attributes for knowledge-based development in dynamic scheduling environments. International Journal of Production Research 34(6), 1739–1755 (1996)

    Article  MATH  Google Scholar 

  8. Conway, R.W., Maxwell, W.L., Miller, L.W.: Theory of scheduling. Dover Publications (2003)

    Google Scholar 

  9. Davenport Andrew, J., Beck, J.C.: A survey of techniques for scheduling with uncertainty. Technical report, IBM and ILOG (2000)

    Google Scholar 

  10. Erol, R., Sahin, C., Baykasoglu, A., Kaplanoglu, V.: A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems. Appl. Software Computing 12, 1720–1732 (2012)

    Article  Google Scholar 

  11. Esquirol, P., Lopez, P.: L’ordonnancement. Economica, Paris (1999)

    Google Scholar 

  12. Huyet, A.L.: Optimization and analysis aid via data-mining for simulated production system». European Journal of Operational Research 173, 827–838 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  13. Katalinic, B., Kordic, V.: Bionic assembly system: Concept, structure and function. In: Proceedings of the 5th IDMME, Bath, UK (2004)

    Google Scholar 

  14. Li, X., Olafsson, S.: Discovering dispatching rules using data mining. Journal of Scheduling 8, 515–527 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  15. Monostori, L., Csáji, B.C., Kádár, B., Pfeiffer, A., Ilie-Zudor, E., Kemény, Z., Szathmári, M.: Towards adaptive and digital manufacturing. Annu. Rev. Control 34, 118–128 (2010)

    Article  Google Scholar 

  16. Mouelhi-Chibani, W., Pierreval, H.: Training a neural network to select dispatching rules in real time. Comput. Ind. Eng. 58, 249–256 (2010)

    Article  Google Scholar 

  17. Olafsson, S., Li, X.: Learning effective new single machine dispatching rules from optimal scheduling data. Int. J. Prod. Econ. 128, 118–126 (2010)

    Article  Google Scholar 

  18. Premalatha, S., Baskar, N.: Implementation of supervised statistical data mining algorithm for single machine scheduling. J. Adv. Manag. Res. 9, 170–177 (2012)

    Article  Google Scholar 

  19. Priore, P., Garcia, D.D., Quesada, I.F.: Manufacturing systems scheduling through machine learning. In: Neural Computation, NC 1998, Vienna, Austria, pp. 914–917 (1998)

    Google Scholar 

  20. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufman Publishers (1993)

    Google Scholar 

  21. Russell, T., Malik, A.M., Chase, M., van Beek, P.: Learning heuristics for the superb-lock instruction scheduling problem. IEEE Transactions on Knowledge and Data Engineering 21(10), 1489–1502 (2009)

    Article  Google Scholar 

  22. Shahzad, A., Mebarki, N.: Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem. Eng. Appl. Artif. Intell. 25, 1173–1181 (2012)

    Article  Google Scholar 

  23. Thomas, P., Thomas, A., Suhner, M.-C.: A neural network for the reduction of a product-driven system emulation model. Prod. Plan. Control 22, 767–781 (2011)

    Article  Google Scholar 

  24. Trentesaux, D., Pesin, P., Tahon, C.: Distributed artificial intelligence for FMS scheduling, control and design support. Journal of Intelligent Manufacturing 11, 573–589 (2000)

    Article  Google Scholar 

  25. Varela, M.L.R., Aparício, J.N., Silva, S., do, C.: A Distributed Knowledge Base for Manufacturing Scheduling. In: Camarinha-Matos, L.M. (ed.) Emerging Solutions for Future Manufacturing Systems. IFIP, pp. 323–330. Springer, Boston (2005)

    Chapter  Google Scholar 

  26. Wang, K., Tong, S., Eynard, B., Roucoules, L., Matta, N.: Review on application of data mining in product design and manufacturing. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 4, pp. 613–618 (2007)

    Google Scholar 

  27. Zambrano, G.R., Pach, C., Aissani, N., Bekrar, A., Berger, T., Trentesaux, T.: Control of myopic behaviour in heterarchical production systems: a holonic based framework. EAAI: Journal of Intelligent Manufacturing 22, 1089–1103 (2012), doi:10.1007/s10845-011-0580-y

    Google Scholar 

  28. Zighed, D.A., Rakotomalala, R.: Graphes d’induction: apprentissage et data mining. Hermes Science Publications (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratoire d’informatique d’Oran “LIO”, Département d’informatique, Faculté des sciences, Université d’Oran, BP 1524, EL M’Naouer, Es Senia, 31000, Oran, Algérie

    Nassima Aissani, Baghdad Atmani & Bouziane Beldjilali

  2. Research: “Production, Services, Information” - PSI Team, TEMPO Lab. Res. Centre EA4542, UVHC, Le Mont Houy, F-59313, Valenciennes cedex 9, France

    Damien Trentesaux

Authors
  1. Nassima Aissani
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  2. Baghdad Atmani
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  3. Damien Trentesaux
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  4. Bouziane Beldjilali
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Corresponding author

Correspondence to Nassima Aissani .

Editor information

Editors and Affiliations

  1. Faculty of Automatic Control and Computer Science (Automatica), University Politehnica of Bucharest, Bucharest, Romania

    Theodor Borangiu

  2. PSI/TEMPO Lab, University of Valenciennes, Valenciennes cedex 9, France

    Damien Trentesaux

  3. Centre de Recherche en Automatique de Nancy, ENSTIB, Epinal cedex 9, France

    Andre Thomas

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Aissani, N., Atmani, B., Trentesaux, D., Beldjilali, B. (2014). Extraction of Priority Rules for Boolean Induction in Distributed Manufacturing Control. In: Borangiu, T., Trentesaux, D., Thomas, A. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing and Robotics. Studies in Computational Intelligence, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-319-04735-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-04735-5_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04734-8

  • Online ISBN: 978-3-319-04735-5

  • eBook Packages: EngineeringEngineering (R0)

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