Scenarios in the Design of Flexible Manufacturing Systems

  • Twan BastenEmail author
  • João Bastos
  • Róbinson Medina
  • Bram van der Sanden
  • Marc C. W. Geilen
  • Dip Goswami
  • Michel A. Reniers
  • Sander Stuijk
  • Jeroen P. M. Voeten


Modern high-tech flexible manufacturing systems (FMS) such as lithography systems, professional printers, X-ray machines, and electron microscopes are characterized by an increasingly tight coupling between machine control software and the controlled physical processes. Control software and the design and configuration of FMS have an important impact on system productivity and product quality. Model-based, scenario-based design provides means for guaranteeing and optimizing system productivity while ensuring its proper functioning. We show that abstract system-level activity models, semantically grounded in (max,+) algebra with activities capturing execution scenarios of the FMS, can be used for fast and accurate productivity analysis of FMS in early design phases. The same models can be used for supervisory controller synthesis and optimization, providing safety and performance guarantees in the supervisory control software. Finally, scenario-based, adaptive, pipelined control enables optimization of data-intensive control loops in FMS, which in turn impacts system-level productivity.


FMS Cyber-physical systems Model-driven design Timing analysis Performance optimization Early design-space exploration Activity modeling Max-plus algebra Supervisory controller synthesis Data-intensive feedback control 



This research is supported in part by the Netherlands Organisation for Scientific Research (NWO), through the Robust Cyber-Physical Systems (RCPS) program, projects 12694 and 12697.


  1. 1.
    S. Adyanthaya, H. Alizadeh Ara, J. Bastos, A. Behrouzian, R. Medina Sánchez, J. van Pinxten, B. van der Sanden, U. Waqas, T. Basten, H. Corporaal, R. Frijns, M. Geilen, D. Goswami, M. Hendriks, S. Stuijk, M. Reniers, J. Voeten, xCPS: a tool to eXplore cyber physical systems. ACM SIGBED Rev. 14(1), 81–95 (2016). Scholar
  2. 2.
    R. Alur, Principles of Cyber-Physical Systems (MIT Press, Cambridge, 2015)Google Scholar
  3. 3.
    R. Alur, D. Dill, A theory of timed automata. Theor. Comput. Sci. 126, 183–235 (1994)MathSciNetCrossRefGoogle Scholar
  4. 4.
    K.J. Åström, B. Wittenmark, Computer-controlled Systems, 3rd edn. (Prentice-Hall, Bergen, 1997)Google Scholar
  5. 5.
    F. Baccelli, G. Cohen, G. Olsder, J. Quadrat, Synchronization and Linearity (Wiley, Hoboken, 1992)zbMATHGoogle Scholar
  6. 6.
    J. Baeten, D. van Beek, P. Cuijpers, M. Reniers, J. Rooda, R. Schiffelers, R. Theunissen, Model-based engineering of embedded systems using the hybrid process algebra chi. Electron. Notes Theor. Comput. Sci. 209, 21–53 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    J. Bastos, S. Stuijk, J. Voeten, R. Schiffelers, J. Jacobs, H. Corporaal, Modeling resource sharing using FSM-SADF, in 2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign, MEMOCODE 2015 (IEEE CS, Piscataway, 2015), pp. 96–101Google Scholar
  8. 8.
    J. Bastos, S. Stuijk, J. Voeten, R. Schiffelers, H. Corporaal, Exploiting specification modularity to prune the optimization-space of manufacturing systems, in Proceedings of the Software and Compilers for Embedded Systems, SCOPES 2018 (ACM, New York, 2018)Google Scholar
  9. 9.
    G. Behrmann, A. Fehnker, T. Hune, K. Larsen, P. Pettersson, J. Romijn, F. Vaandrager, Minimum-cost reachability for priced time automata, in Proceedings of the Hybrid systems: computation and control, HSCC 2001. LNCS, vol. 2034 (Springer, Berlin, 2001), pp. 147–161Google Scholar
  10. 10.
    C.G. Cassandras, S. Lafortune, Introduction to Discrete Event Systems, 2nd edn. (Springer, Berlin, 2010)zbMATHGoogle Scholar
  11. 11.
    L. Denissen, Image-based Control and Throughput Analysis for Flexible Manufacturing Systems. Master’s thesis, Eindhoven University of Technology (2016)Google Scholar
  12. 12.
    E.W. Dijkstra, A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  13. 13.
    S. Gaubert, Performance evaluation of (max,+) automata. IEEE Trans. Autom. Control 40(12), 2014–2015 (1995)MathSciNetCrossRefGoogle Scholar
  14. 14.
    M. Geilen, Synchronous dataflow scenarios. ACM Trans. Embed. Comput. Syst. 10(2), 16:1–16:31 (2010)Google Scholar
  15. 15.
    M. Geilen, S. Stuijk, Worst-case performance analysis of synchronous dataflow scenarios, in Proceedings of the Hardware/Software Codesign and System Synthesis, International Conference, CODES+ISSS 2010 (ACM, New York, 2010), pp. 125–134Google Scholar
  16. 16.
    M. Geilen, J. Falk, C. Haubelt, T. Basten, B. Theelen, S. Stuijk, Performance analysis of weakly-consistent scenario-aware dataflow graphs. J. Signal Process. Syst. 87(1), 157–175 (2017)CrossRefGoogle Scholar
  17. 17.
    M. Hendriks, B. van den Nieuwelaar, F. Vaandrager, Model checker aided design of a controller for a wafer scanner. Int. J. Softw. Tools Technol. Trans. 8(6), 633–647 (2006)CrossRefGoogle Scholar
  18. 18.
    M. Hendriks, J. Verriet, T. Basten, M. Brassé, R. Dankers, R. Laan, A. Lint, H. Moneva, L. Somers, M. Willekens, Performance engineering for industrial embedded data-processing systems, in Proceedings of the Product-Focused Software Process Improvement, PROFES 2015, International Conference. LNCS, vol. 9459 (Springer, Berlin, 2015), pp. 399–414Google Scholar
  19. 19.
    J. Huang, J. Voeten, H. Corporaal, Predictable real-time software synthesis. Real-Time Syst. J. 36(3), 159–198 (2007)CrossRefGoogle Scholar
  20. 20.
    J. Huang, J. Voeten, M. Groothuis, J. Broenink, H. Corporaal, A model-driven design approach for mechatronic systems, in Proceedings of the Application of Concurrency to System Design, ACSD 2007, 7th International Conference (IEEE CS, Piscataway, 2007), pp. 127–136Google Scholar
  21. 21.
    J.E. Kelley Jr, M.R. Walker, Critical-path planning and scheduling, in Papers Presented at the December 1–3, 1959, Eastern Joint IRE-AIEE-ACM Computer Conference, IRE-AIEE-ACM ’59 (Eastern) (ACM, New York, 1959), pp. 160–173. Scholar
  22. 22.
    E. Lee, M. Niknami, T. Nouidui, M. Wetter, Modeling and Simulating cyber-physical systems using CyPhySim, in Proceedings of the International Conference on Embedded Software, EMSOFT 2015 (IEEE CS, Piscataway, 2015)Google Scholar
  23. 23.
    J. Markovski, D. van Beek, R. Theunissen, K. Jacobs, J. Rooda, A state-based framework for supervisory control synthesis and verification, in Proceedings of the Decision and Control, 2010 49th IEEE Conference on, CDC (2010), pp. 3481–3486.
  24. 24.
    O. Mason, R.N. Shorten, On common quadratic Lyapunov functions for stable discrete time LTI systems. IMA J. Appl. Math. 69, 271–283 (2002)MathSciNetCrossRefGoogle Scholar
  25. 25.
    MathWorks, Simulink: Simulation and Model-Based Design (2018).
  26. 26.
    R. Medina, S. Stuijk, D. Goswami, T. Basten, Reconfigurable pipelined sensing for image-based control, in Industrial Embedded Systems, 11th IEEE International Symposium, SIES 2016 (IEEE CS, Piscataway, 2016), pp. 1–8Google Scholar
  27. 27.
    Y. Narahari, N. Viswanadham, A Petri net approach to the modelling and analysis of flexible manufacturing systems. Ann. Oper. Res. 3, 449–472 (1985)CrossRefGoogle Scholar
  28. 28.
  29. 29.
    L. Ouedraogo, R. Kumar, R. Malik, K. Akesson, Nonblocking and safe control of discrete-event systems modeled as extended finite automata. IEEE Trans. Autom. Sci. Eng. 8(3), 560–569 (2011). Scholar
  30. 30.
    D. Peled, P. Pelliccione, P. Spoletini, Model checking, in Wiley Encyclopedia of Computer Science and Engineering (Wiley, Hoboken, 2009)Google Scholar
  31. 31.
    A. Rahatulain, T. Qureshi, M. Onori, Modeling and simulation of evolvable production systems using Simulink/SimEvents, in Proceeding of the 40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014 (IEEE CS, Piscataway, 2014), pp. 2591–2596Google Scholar
  32. 32.
    P.J.G. Ramadge, W.M. Wonham, Supervisory control of a class of discrete event processes. SIAM J. Control. Optim. 25(1), 206–230 (1987)MathSciNetCrossRefGoogle Scholar
  33. 33.
    P.J.G. Ramadge, W.M. Wonham, The control of discrete event systems. Proc. IEEE 77(1), 81–98 (1989)CrossRefGoogle Scholar
  34. 34.
    M. Silva, R. Valette, Petri nets and flexible manufacturing, in Advances in Petri Nets 1989, LNCS 424 (Springer, Berlin, 1989), pp. 374–417Google Scholar
  35. 35.
    M. Skoldstam, K. Åkesson, M. Fabian, Modeling of discrete event systems using finite automata with variables, in Proceeding of the Decision and Control, 2007 46th IEEE Conference on, CDC (2007), pp. 3387–3392.
  36. 36.
    S. Stuijk, M. Geilen, B. Theelen, T. Basten, Scenario-aware dataflow: modeling, analysis and implementation of dynamic applications, in Proceeding of the Embedded Computer Systems: Architectures, Modeling, and Simulation, International Conference, IC-SAMOS 11 (IEEE CS, Piscataway, 2011), pp. 404–411Google Scholar
  37. 37.
    Z. Sun, S.S. Ge, Stability Theory of Switched Dynamical Systems (Springer, Berlin, 2011)CrossRefGoogle Scholar
  38. 38.
    Uppaal (2018).
  39. 39.
    J. Valencia, D. Goswami, K. Goossens, Composable platform-aware embedded control systems on a multi-core architecture, in Proceeding of the Digital System Design, 2015 Euromicro Conference on (IEEE, Piscataway, 2015), pp. 502–509Google Scholar
  40. 40.
    B. van der Sanden, Performance Analysis and Optimization of Supervisory Controllers. Ph.D. thesis (Eindhoven University of Technology, Eindhoven, 2018)Google Scholar
  41. 41.
    B. van der Sanden, M. Reniers, M. Geilen, T. Basten, J. Jacobs, J. Voeten, R. Schiffelers, Modular model-based supervisory controller design for wafer logistics in lithography machines, in Proceeding of the ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems, MODELS 2015 (IEEE CS, Piscataway, 2015), pp. 416–425Google Scholar
  42. 42.
    B. van der Sanden, J. Bastos, J. Voeten, M. Geilen, M. Reniers, T. Basten, J. Jacobs, R. Schiffelers, Compositional specification of functionality and timing of manufacturing systems, in Forum on Specification & Design Languages, FDL 2016, Proceeding IEEE (CS, Piscataway, 2016). Scholar
  43. 43.
    B. van der Sanden, M. Geilen, M. Reniers, T. Basten, Partial-order reduction for performance analysis of max-plus timed systems, in Proceeding of the Application of Concurrency to System Design, 18th International Conference, ACSD 2018 (IEEE CS, Piscataway, 2018), pp. 40–49Google Scholar
  44. 44.
    J. Xing, B. Theelen, R. Langerak, J. van de Pol, J. Tretmans, J. Voeten, UPPAAL in practice: quantitative verification of a RapidIO network, in Proceeding of the Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2010, 4th International Symposium. LNCS, vol. 6416 (Springer, Berlin, 2010), pp. 160–174.Google Scholar
  45. 45.
    M. Zhou, K. Venkatesh, Modeling, Simulation, and Control of Flexible Manufacturing Systems: A Petri Net Approach (World Scientific, Singapore, 1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Twan Basten
    • 1
    Email author
  • João Bastos
    • 2
  • Róbinson Medina
    • 2
  • Bram van der Sanden
    • 2
  • Marc C. W. Geilen
    • 2
  • Dip Goswami
    • 2
  • Michel A. Reniers
    • 2
  • Sander Stuijk
    • 2
  • Jeroen P. M. Voeten
    • 3
    • 4
  1. 1.Eindhoven University of Technology and ESI, TNOEindhovenThe Netherlands
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Eindhoven University of TechnologyEindhovenThe Netherlands
  4. 4.ESI, TNOEindhovenThe Netherlands

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