Encyclopedia of Systems and Control

2015 Edition
| Editors: John Baillieul, Tariq Samad

Applications of Discrete-Event Systems

  • Spyros Reveliotis
Reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5058-9_59


This entry provides an overview of the problems addressed by discrete-event systems (DES) theory, with an emphasis on their connection to various application contexts. The primary intentions are to reveal the caliber and the strengths of this theory and to direct the interested reader, through the listed citations, to the corresponding literature. The concluding part of the entry also identifies some remaining challenges and further opportunities for the area.


Applications Discrete-event systems 
This is a preview of subscription content, log in to check access.


  1. Akesson K, Fabian M, Flordal H, Malik R (2006) SUPREMICA-an integrated environment for verification, synthesis and simulation of discrete event systems. In: Proceedings of the 8th international workshop on discrete event systems, Ann Arbor. IEEE, pp 384–385Google Scholar
  2. Alenljung T, Lennartson B, Hosseini MN (2012) Sensor graphs for discrete event modeling applied to formal verification of PLCs. IEEE Trans Control Syst Technol 20:1506–1521Google Scholar
  3. Andersson K, Richardsson J, Lennartson B, Fabian M (2010) Coordination of operations by relation extraction for manufacturing cell controllers. IEEE Trans Control Syst Technol 18: 414–429Google Scholar
  4. Baccelli F, Cohen G, Olsder GJ, Quadrat JP (1992) Synchronization and linearity: an algebra for discrete event systems. Wiley, New YorkGoogle Scholar
  5. Balemi S, Hoffmann GJ, Wong-Toi PG, Franklin GJ (1993) Supervisory control of a rapid thermal multiprocessor. IEEE Trans Autom Control 38:1040–1059MathSciNetGoogle Scholar
  6. Banks J, Carson II JS, Nelson BL, Nicol DM (2009) Discrete-event system simulation, 5th edn. Prentice Hall, Upper SaddleGoogle Scholar
  7. Bertsekas DP (1995) Dynamic programming and optimal control, vols 1, 2. Athena Scientific, BelmontGoogle Scholar
  8. Brandin B (1996) The real-time supervisory control of an experimental manufacturing cell. IEEE Trans Robot Autom 12:1–14Google Scholar
  9. Cao X-R (2005) Basic ideas for event-based optimization of Markov systems. Discret Event Syst Theory Appl 15:169–197Google Scholar
  10. Cao X-R (2007) Stochastic learning and optimization: a sensitivity approach. Springer, New YorkGoogle Scholar
  11. Cassandras CG (1994) Perturbation analysis and “rapid learning” in the control of manufacturing systems. In: Leondes CT (ed) Dynamics of discrete event systems, vol 51. Academic, Boston, pp 243–284Google Scholar
  12. Cassandras CG, Lafortune S (2008) Introduction to discrete event systems, 2nd edn. Springer, New YorkGoogle Scholar
  13. Cassandras CG, Strickland SG (1988) Perturbation analytic methodologies for design and optimization of communication networks. IEEE J Sel Areas Commun 6:158–171Google Scholar
  14. Cassandras CG, Yao C (2013) Hybrid models for the control and optimization of manufacturing systems. In: Campos J, Seatzu C, Xie X (eds) Formal methods in manufacturing. CRC/Taylor and Francis, Boca RatonGoogle Scholar
  15. Chandra V, Huang Z, Kumar R (2003) Automated control synthesis for an assembly line using discrete event system theory. IEEE Trans Syst Man Cybern Part C 33:284–289Google Scholar
  16. Curry JER (2012) Some perspectives and challenges in the (discrete) control of cellular systems. In: Proceedings of the WODES 2012, Guadalajar. IFAC, pp 1–3Google Scholar
  17. Dai JG (1995) On positive Harris recurrence of multiclass queueing networks: a unified approach via fluid limit models. Ann Appl Probab 5:49–77MathSciNetGoogle Scholar
  18. David R, Alla H (1992) Petri nets and Grafcet: tools for modelling discrete event systems. Prentice-Hall, Upper SaddleGoogle Scholar
  19. David R, Alla H (2005) Discrete, continuous and hybrid Petri nets. Springer, BerlinGoogle Scholar
  20. David-Henriet X, Hardouin L, Raisch J, Cottenceau B (2013) Optimal control for timed event graphs under partial synchronization. In: Proceedings of the 52nd IEEE conference on decision and control, Florence. IEEEGoogle Scholar
  21. Dubreil J, Darondeau P, Marchand H (2010) Supervisory control for opacity. IEEE Trans Autom Control 55:1089–1100MathSciNetGoogle Scholar
  22. Endsley EW, Almeida EE, Tilbury DM (2006) Modular finite state machines: development and application to reconfigurable manufacturing cell controller generation. Control Eng Pract 14:1127–1142Google Scholar
  23. Ezpeleta J, Colom JM, Martinez J (1995) A Petri net based deadlock prevention policy for flexible manufacturing systems. IEEE Trans R&A 11:173–184Google Scholar
  24. Feng L, Wonham WM (2006) TCT: a computation tool for supervisory control synthesis. In: Proceedings of the 8th international workshop on discrete event systems, Ann Arbor. IEEE, pp 388–389Google Scholar
  25. Feng L, Wonham WM, Thiagarajan PS (2007) Designing communicating transaction processes by supervisory control theory. Formal Methods Syst Design 30:117–141Google Scholar
  26. Fu M, Xie X (2002) Derivative estimation for buffer capacity of continuous transfer lines subject to operation-dependent failures. Discret Event Syst Theory Appl 12:447–469MathSciNetGoogle Scholar
  27. Gershwin SB (1994) Manufacturing systems engineering. Prentice Hall, Englewood CliffsGoogle Scholar
  28. Giua A, Fanti MP, Seatzu C (2006) Monitor design for colored Petri nets: an application to deadlock prevention in railway networks. Control Eng Pract 10:1231–1247Google Scholar
  29. Hill RC, Cury JER, de Queiroz MH, Tilbury DM, Lafortune S (2010) Multi-level hierarchical interface-based supervisory control. Automatica 46:1152–1164Google Scholar
  30. Ho YC, Cao X-R (1991) Perturbation analysis of discrete event systems. Kluwer Academic, BostonGoogle Scholar
  31. Homem-de Mello T, Shapiro A, Spearman ML (1999) Finding optimal material release times using simulation-based optimization. Manage Sci 45:86–102Google Scholar
  32. Hopcroft JE, Ullman JD (1979) Introduction to automata theory, languages and computation. Addison-Wesley, ReadingGoogle Scholar
  33. Horowitz R, Varaiya P (2000) Control design of automated highway system. Proc IEEE 88: 913–925Google Scholar
  34. Jeng M, Xie X, Peng MY (2002) Process nets with resources for manufacturing modeling and their analysis. IEEE Trans Robot Autom 18:875–889Google Scholar
  35. Kim J-H, Lee T-E (2012) Feedback control design for cluster tools with wafer residency time constraints. In: IEEE conference on systems, man and cybernetics, Seoul. IEEE, pp 3063–3068Google Scholar
  36. Kumar R, Takai S (2010) Decentralized prognosis of failures in discrete event systems. IEEE Trans Autom Control 55:48–59MathSciNetGoogle Scholar
  37. Lee T-E (2008) A review of cluster tool scheduling and control for semiconductor manufacturing. In: Proceedings of the winter simulation conference, Miami. INFORMS, pp 1–6Google Scholar
  38. Lewis RW (1998) Programming industrial control systems using IEC 1131-3. Technical report, The Institution of Electrical EngineersGoogle Scholar
  39. Li M, Kumar R (2012) Model-based automatic test generation for Simulink/Stateflow using extended finite automaton. In: Proceedings of the CASE, Seoul. IEEEGoogle Scholar
  40. Li R, Reveliotis S (2013) Performance optimization for a class of generalized stochastic Petri nets. In: Proceedings of the 52nd IEEE conference on decision and control, Florence. IEEEGoogle Scholar
  41. Li Z, Zhou M, Wu N (2008) A survey and comparison of Petri net-based deadlock prevention policies for flexible manufacturing systems. IEEE Trans Syst Man Cybern Part C 38:173–188Google Scholar
  42. Liao H, Wang Y, Cho HK, Stanley J, Kelly T, Lafortune S, Mahlke S, Reveliotis S (2013) Concurrency bugs in multithreaded software: modeling and analysis using Petri nets. Discret Event Syst Theory Appl 23:157–195MathSciNetGoogle Scholar
  43. Markovski J, Su R (2013) Towards optimal supervisory controller synthesis of stochastic nondeterministic discrete event systems. In: Proceedings of the 52nd IEEE conference on decision and control, Florence. IEEEGoogle Scholar
  44. Meyn S (2008) Control techniques for complex networks. Cambridge University Press, CambridgeGoogle Scholar
  45. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77:541–580Google Scholar
  46. Panayiotou CG, Cassandras CG (1999) Optimization of kanban-based manufacturing systems. Automatica 35:1521–1533MathSciNetGoogle Scholar
  47. Park E, Tilbury DM, Khargonekar PP (1999) Modular logic controllers for machining systems: formal representations and performance analysis using Petri nets. IEEE Trans Robot Autom 15:1046–1061Google Scholar
  48. Pinedo M (2002) Scheduling. Prentice Hall, Upper Saddle RiverGoogle Scholar
  49. Reveliotis SA (2000) Conflict resolution in AGV systems. IIE Trans 32(7):647–659Google Scholar
  50. Reveliotis SA (2005) Real-time management of resource allocation systems: a discrete event systems approach. Springer, New YorkGoogle Scholar
  51. Reveliotis SA (2007) Algebraic deadlock avoidance policies for sequential resource allocation systems. In: Lahmar M (ed) Facility logistics: approaches and solutions to next generation challenges. Auerbach Publications, Boca Raton, pp 235–289Google Scholar
  52. Reveliotis SA, Ferreira PM (1996) Deadlock avoidance policies for automated manufacturing cells. IEEE Trans Robot Autom 12:845–857Google Scholar
  53. Reveliotis S, Nazeem A (2013) Deadlock avoidance policies for automated manufacturing systems using finite state automata. In: Campos J, Seatzu C, Xie X (eds) Formal methods in manufacturing. CRC/Taylor and FrancisGoogle Scholar
  54. Reveliotis S, Roszkowska E (2011) Conflict resolution in free-ranging multi-vehicle systems: a resource allocation paradigm. IEEE Trans Robot 27:283–296Google Scholar
  55. Ricker L, Lafortune S, Gene S (2006) DESUMA: a tool integrating giddes and umdes. In: Proceedings of the 8th international workshop on discrete event systems, Ann Arbor. IEEE, pp 392–393Google Scholar
  56. Saboori A, Hadjicostis CN (2012) Opacity-enforcing supervisory strategies via state estimator constructions. IEEE Trans Autom Control 57:1155–1165MathSciNetGoogle Scholar
  57. Saboori A, Hadjicostis CN (2014) Current-state opacity formulations in probabilistic finite automata. IEEE Trans Autom Control 59:120–133MathSciNetGoogle Scholar
  58. Sampath M, Sengupta R, Lafortune S, Sinnamohideen K, Teneketzis D (1996) Failure diagnosis using disrcete event models. IEEE Trans Control Syst Technol 4:105–124Google Scholar
  59. Sampath R, Darabi H, Buy U, Liu J (2008) Control reconfiguration of discrete event systems with dynamic control specifications. IEEE Trans Autom Sci Eng 5:84–100Google Scholar
  60. Santoso T, Ahmed S, Goetschalckx M, Shapiro A (2005) A stochastic programming approach for supply chain network design under uncertainty. Europ J Oper Res 167:96–115MathSciNetGoogle Scholar
  61. Schmidt K (2012) Computation of supervisors for reconfigurable machine tools. In: Proceedings of the WODES 2012, Guadalajara. IFAC, pp 227–232Google Scholar
  62. Sethi SP, Zhang Q (1994) Hierarchical decision making in stochastic manufacturing systems. Birkhäuser, BostonGoogle Scholar
  63. Srikant R (2004) The mathematics of internet congestion control. Birkhäuser, BostonGoogle Scholar
  64. Van der Aalst W (1997) Verification of workflow nets. In: Azema P, Balbo G (eds) Lecture notes in computer science, vol 1248. Springer, New York, pp 407–426Google Scholar
  65. Wardi Y, Cassandras CG (2013) Approximate IPA: trading unbiasedness for simplicity. In: Proceedings of the 52nd IEEE conference on decision and control, Florence. IEEEGoogle Scholar
  66. Wassyng A, Lawford M, Maibaum T (2011) Software certification experience in the Canadian muclear industry: lessons for the future. In: EMSOFT’11, TaipeiGoogle Scholar
  67. Wightkin N, Guy U, Darabi H (2011) Formal modeling of sequential function charts with time Petri nets. IEEE Trans Control Syst Technol 19:455–464Google Scholar
  68. Wonham WM (2006) Supervisory control of discrete event systems. Technical report ECE 1636F/1637S 2006-07, Electrical & Computer Eng., University of TorontoGoogle Scholar
  69. Zhou M, Fanti MP (eds) (2004) Deadlock resolution in computer-integrated systems. Marcel Dekker, SingaporeGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Spyros Reveliotis
    • 1
  1. 1.School of Industrial & Systems Engineering, Georgia Institute of TechnologyAtlantaUSA