Invariant-Based Production Control Reviewed: Mixing Hierarchical and Heterarchical Control in Flexible Job Shop Environments

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9266)

Abstract

We are interested in the interplay of hierarchical and heterarchical control to reduce myopic behavior in a setting where central planning establishes relaxed schedules and distributed control is applied to make remaining decisions at runtime. We therefore pick up an idea introduced by Bongaerts et al. [4] to generate invariants, relaxed schedules, as constraints on distributed production control.

We apply this concept to the Flexible Job Shop Scheduling Problem (FJSSP), represented as disjunct graphs, introduce a measure to quantify the “tightness” of invariants, constrains the set of local decision heuristics that can be applied in such setting and present a simulation implementation, based on standard problem instances and optimization models with initial results. They validate the proposed measure and highlighting the need for further investigation of the interplay between problem structure and achieved performance.

Keywords

Production control Invariants Scheduling Distributed decision making 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barnes, J.W., Chambers, J.B.: Solving the job shop scheduling problem with tabu search. IIE Transactions 27(2), 257–263 (1995)CrossRefGoogle Scholar
  2. 2.
    Behnke, D., Geiger, M.J.: Test instances for the flexible job shop scheduling problem with work centers. Tech. Rep. 12–01-01, Helmut-Schmidt Universität der Bundeswehr Hamburg, Lehrstuhl für Betriebswirtschaftslehre, insbes. Logistik-Management (May 2012), http://edoc.sub.uni-hamburg.de/hsu/volltexte/2012/2982/
  3. 3.
    Błażewicz, J., Pesch, E., Sterna, M.: The disjunctive graph machine representation of the job shop scheduling problem. European Journal of Operational Research 127(2), 317–331 (2000)CrossRefMATHGoogle Scholar
  4. 4.
    Bongaerts, L., Monostori, L., McFarlane, D., Kádár, B.: Hierarchy in distributed shop floor control. Computers in Industry 43(2), 123–137 (2000)CrossRefGoogle Scholar
  5. 5.
    Brandimarte, P.: Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research 41(3), 157–183 (1993)CrossRefGoogle Scholar
  6. 6.
    Brennan, R.W.: Performance comparison and analysis of reactive and planning-based control architectures for manufacturing. Robotics and Computer-Integrated Manufacturing 16(2–3), 191–200 (2000)CrossRefGoogle Scholar
  7. 7.
    Brennan, R.W., Norrie, D.H.: Metrics for evaluating distributed manufacturing control systems. Computers in Industry 51(2), 225–235 (2003). virtual Enterprise ManagementCrossRefGoogle Scholar
  8. 8.
    Cavalieri, S., Garetti, M., Macchi, M., Taisch, M.: An experimental benchmarking of two multi-agent architectures for production scheduling and control. Computers in Industry 43(2), 139–152 (2000)CrossRefMATHGoogle Scholar
  9. 9.
    Dauzère-Pérès, S., Paulli, J.: An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Annals of Operations Research 70(1–4), 281–306 (1997)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Dauzère-Pérès, S., Roux, W., Lasserre, J.: Multi-resource shop scheduling with resource flexibility. European Journal of Operational Research 107(2), 289–305 (1998)CrossRefGoogle Scholar
  11. 11.
    Dilts, D., Boyd, N., Whorms, H.: The evolution of control architectures for automated manufacturing systems. Journal of Manufacturing Systems 10(1), 79–93 (1991)CrossRefGoogle Scholar
  12. 12.
    Dubey, P.: Inefficiency of nash equilibria. Mathematics of Operations Research 11(1), 1–8 (1986)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Fattahi, P., Saidi Mehrabad, M., Jolai, F.: Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing 18(3), 331–342 (2007)CrossRefGoogle Scholar
  14. 14.
    Grundstein, S., Schukraft, S., Scholz-Reiter, B., Freitag, M.: Coupling order release methods with autonomous control methods – an assessment of potentials by literature review and discrete event simulation. International Journal of Production Management and Engineering 3(1), 43 (2015)Google Scholar
  15. 15.
    Hadzhiev, B., Windt, K., Bergholz, W., Hütt, M.T.: A model of graph coloring dynamics with attention waves and strategic waiting. Advances in Complex Systems 12(6), 549–564 (2009)CrossRefMATHGoogle Scholar
  16. 16.
    Hurink, J., Jurisch, B., Thole, M.: Tabu search for the job-shop scheduling problem with multi-purpose machines. Operations-Research-Spektrum 15(4), 205–215 (1994)Google Scholar
  17. 17.
    Lin, G.Y.J., Solberg, J.J.: Effectiveness of flexible routing control. International Journal of Flexible Manufacturing Systems 3(3–4), 189–211 (1991)Google Scholar
  18. 18.
    Mönch, L., Drießel, R.: A distributed shifting bottleneck heuristic for complex job shops. Computers & Industrial Engineering 49(3), 363–380 (2005)CrossRefGoogle Scholar
  19. 19.
    Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling 12(4), 417–431 (2009)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Philipp, T., Böse, F., Windt, K.: Evaluation of autonomously controlled logistic processes. In: Proceedings of 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, Ischia, Italy, pp. 347–352 (2006)Google Scholar
  21. 21.
    Pochet, Y., Wolsey, L.A.: Production Planning by Mixed Integer Programming. Springer Series in Operations Research and Financial Engineering. Springer, New York (2006)Google Scholar
  22. 22.
    Puget, J.F.: Solving flexible job shop scheduling problems (November 2013). https://www.ibm.com/developerworks/community/blogs/jfp/entry/solving_flexible_job_shop_scheduling_problems?lang=en (accessed: March 10, 2015)
  23. 23.
    Roy, B., Sussmann, B.: Les problèmes d’ordonnancement avec contraintes disjonctives. Note DS 9 (1964)Google Scholar
  24. 24.
    Schneeweiß, C.: Distributed decision making, 2 edn. Springer (2003)Google Scholar
  25. 25.
    Scholz-Reiter, B., Görges, M., Philipp, T.: Autonomously controlled production systems - influence of autonomous control level on logistic performance. CIRP Annals - Manufacturing Technology 58(1), 395–398 (2009)CrossRefGoogle Scholar
  26. 26.
    Tay, J.C., Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering 54(3), 453–473 (2008)CrossRefGoogle Scholar
  27. 27.
    Trentesaux, D.: Distributed control of production systems. Engineering Applications of Artificial Intelligence 22(7), 971–978 (2009). distributed Control of Production SystemsCrossRefGoogle Scholar
  28. 28.
    Vrabič, R., Husejnagić, D., Butala, P.: Discovering autonomous structures within complex networks of work systems. CIRP Annals - Manufacturing Technology 61(1), 423–426 (2012)CrossRefGoogle Scholar
  29. 29.
    Zambrano Rey, G., Bonte, T., Prabhu, V., Trentesaux, D.: Reducing myopic behavior in fms control: A semi-heterarchical simulationoptimization approach. Simulation Modelling Practice and Theory 46, 53–75 (2014). simulation-Optimization of Complex Systems: Methods and ApplicationsGoogle Scholar
  30. 30.
    Zambrano Rey, G., Pach, C., Aissani, N., Bekrar, A., Berger, T., Trentesaux, D.: The control of myopic behavior in semi-heterarchical production systems: A holonic framework. Engineering Applications of Artificial Intelligence 26(2), 800–817 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Mathematics and LogisticsJacobs University Bremen gGmbHBremenGermany

Personalised recommendations