Skip to main content
Log in

Time-Optimal Coordination of Flexible Manufacturing Systems Using Deterministic Finite Automata and Mixed Integer Linear Programming

  • Published:
Discrete Event Dynamic Systems Aims and scope Submit manuscript

Abstract

Automation and flexibility are often mentioned as key concepts in modern production industry. To increase the level of flexibility, deterministic finite automata (DFA) can be used to model, specify and verify the production systems. Often, it is also desirable to optimize some production criteria, such as for example the cycle time of a manufacturing cell. In this paper, a method for automatic conversion from DFA to a mixed integer linear programming (MILP) formulation is first presented. This conversion is developed for a number of DFA structures that have shown to be useful in practical applications. Special attention is paid to reducing the search region explored by the MILP solver. Second, a conversion from the MILP solution to a DFA supervisor is described. This allows to combine the advantages of DFA modeling with the efficiency of MILP and supervisory control theory to automatically generate time-optimal, collision-free and non-blocking working schedules for flexible manufacturing systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Adams J, Balas E, Zawack D (1988) The shifting bottleneck procedure for job shop scheduling. Manage Sci 34(3):391–401

    Article  MATH  MathSciNet  Google Scholar 

  • Åkesson K, Fabian M, Flordal H, Malik R (2006) Supremica—an integrated environment for verification, synthesis and simulation of discrete event systems. In: Proc. of the 8th workshop on discrete event systems (WODES’06), Ann Arbor

  • Bayen AM, Tomlin CJ, Yinyu Y, Zhang J (2003) Milp formulation and polynomial time algorithm for an aircraft scheduling problem. In: 42nd IEEE international conference on decision and control, Maui

  • Blazewicz J, Domschke W, Pesch E (1996) The job shop scheduling problem: conventional and new solution techniques. Eur J Oper Res 93:1–33

    Article  MATH  Google Scholar 

  • COIN-OR Branch and Cut (2008) Cbc homepage. http://projects.coin-or.org/Cbc

  • de Berg M, van Kreveld M, Overmars M, Schwarzkopf O (2000) Computational geometry, 2nd edn. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  • Falkman P, Nielsen J, Lennartson B, von Euler-Chelpin A (2008) Generation of step ap214 models from discrete event systems for process planning and control. IEEE Trans Autom Sci Eng 5:113–126

    Article  Google Scholar 

  • Fisher H, Thompson G (1963) Industrial scheduling. In: Muth JF, Thompson GL (eds) Chapter probabilistic learning combinations of local job-shop scheduling rules. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Flordal H, Malik R, Fabian M, Åkesson K (2007) Compositional synthesis of maximally permissive supervisors using supervision equivalence. Discret Event Dyn Syst 17(4):475–504

    Article  MATH  Google Scholar 

  • GNU Linear Programming Kit (2008) GNU Linear Programming Kit homepage. http://www.gnu.org/software/glpk

  • Hoare CAR (1985) Communicating sequential processes. Prentice Hall International Series in Computer Science. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Huang Z, Wu Z (2004) Deadlock-free scheduling for automated manufacturing systems using genetic algorithm and petri nets. In: IEEE international conference on robotics and automation, New Orleans

  • ILOG CPLEX (2008) ILOG homepage. http://www.ilog.com

  • ISO 10303-1 (1994) Industrial automation systems and integration—product data representation and exchange—part 1: overview and fundamental principles. ISO standard

  • Johnson DB (1975) Finding all the elementary circuits of a directed graph. SIAM J Comput 4:77–84

    Article  MATH  MathSciNet  Google Scholar 

  • Kobetski A, Fabian M (2006) Scheduling of discrete event systems using mixed integer linear programming. In: Proc. of the 8th IEEE international workshop on discrete event systems. Ann Arbor

  • Kobetski A, Flordal H, Lennartson B, Fabian M (2009) A framework for automatic generation of interlocking functions for flexible manufacturing. Technical Report, Department of Signals and Systems, Chalmers University of Technology, ISSN 1403-266x; nr R004/2009

  • Kobetski A, Richardsson J, Åkesson K, Fabian M (2007) Minimization of expected cycle time in manufacturing cells with uncontrollable behavior. In: Proc. of the IEEE conference on automation science and engineering, Scottsdale

  • Kobetski A, Spensieri D, Fabian M (2006) Scheduling algorithms for optimal robot cell coordination—a comparison. In: Proc. of the IEEE conference on automation science and engineering, Shanghai

  • Lawley MA (1999) Deadlock avoidance for production systems with flexible routing. Trans Robot Autom 15:497–509

    Article  Google Scholar 

  • Lee DY, DiCesare F (1994) Scheduling flexible manufacturing systems using petri nets and heuristic search. IEEE Trans Robot Autom 10:123–132

    Article  Google Scholar 

  • Li Y, Wonham WM (1994) Control of vector discrete-event systems II—controller synthesis. Trans Automat Contr 39(3):512–531

    Article  MATH  MathSciNet  Google Scholar 

  • Liljenvall T (1998) Scheduling for production systems. Lic. Thesis 293L, School of Electrical and Computer Engineering, Chalmers University of Technology

  • Linderoth JT, Ralphs TK (2005) Noncommercial software for mixed-integer linear programming. In: Karlof J (ed) Integer programming: theory and practice. CRC Press Operations Research Series. CRC, Boca Raton, pp 253–303

    Google Scholar 

  • Liu J, MacCarthy BL (1997) A global milp model for fms scheduling. Eur J Oper Res 100:441–453

    Article  MATH  Google Scholar 

  • Mendez CA, Cerda J (2004) An milp framework for batch reactive scheduling with limited discrete resources. Comput Chem Eng 28:1059–1068

    Article  Google Scholar 

  • Niño-Mora J (2001) Stochastic scheduling. In: Floudas CA, Pardalos PM (eds) Kluwer, Dordrecht, pp 367–372

  • Nowicki E, Smutnicki C (1996) A fast taboo search algorithm for the job shop problem. Manage Sci 42(6):797–813

    Article  MATH  Google Scholar 

  • Peng J, Akella S (2005) Coordinating multiple double integrator robots on a roadmap: convexity and global optimality. In: IEEE international conference on robotics and automation. IEEE, Barcelona, pp 2751–2758

    Chapter  Google Scholar 

  • Ramadge P, Wonham W (1989) The control of discrete event systems. Proc IEEE 77:81–98

    Article  Google Scholar 

  • Reeves CR, Rowe JE (2002) Genetic algorithms—principles and perspectives: a guide to GA theory. Operations Research/Computer Science Interfaces Series. Springer, Berlin Heidelberg New York

    Google Scholar 

  • Reveliotis S, Lawley M, Ferreira P (1997) Polynomial complexity deadlock avoidance policies for sequential resource allocation systems. IEEE Trans Automat Contr 42:1344–1357

    Article  MATH  MathSciNet  Google Scholar 

  • Russell S, Norvig P (1995) Artificial intelligence—a modern approach. Prentice Hall International Editions. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Schouwenaars T, DeMoor B, Feron E, How J (2001) Mixed integer programming for safe multi-vehicle cooperative path planning. In: The 2001 European control conference, Porto

  • Schrijver A (1986) Theory of linear and integer programming. Wiley, Chichester

    MATH  Google Scholar 

  • Segala R (1995) Modeling and verification of randomized distributed real-time systems. PhD thesis, Massachusetts Institute of Technology

  • Silberschatz A, Peterson G (1991) Operating systems concepts. Addison-Wesley, Reading

    Google Scholar 

  • Tarjan R (1972) Depth-first search and linear graph algorithms. SIAM J Comput 1:146–160

    Article  MATH  MathSciNet  Google Scholar 

  • Williams HP (1990) Model building in mathematical programming. Wiley, New York

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Avenir Kobetski.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kobetski, A., Fabian, M. Time-Optimal Coordination of Flexible Manufacturing Systems Using Deterministic Finite Automata and Mixed Integer Linear Programming. Discrete Event Dyn Syst 19, 287–315 (2009). https://doi.org/10.1007/s10626-009-0064-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10626-009-0064-9

Keywords

Navigation