Planning and Scheduling based on Petri Nets

  • Jean-Marie Proth
  • Ioannis Minis
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 310)


The objective of this chapter is to show that Petri nets facilitate a comprehensive approach to production management and reduce the complexity of the problems involved at the expense of some constraints imposed on the decision making system. The introduction provides a content outline of the chapter. In the second section, we introduce different aspects related to Petri nets which will be used in the approaches proposed for the short-term planning and scheduling of manufacturing systems. This section includes, but is not limited to, event graphs, decomposable Petri nets and controlable Petri nets. The third section focuses on cyclic manufacturing systems. For this type of systems, it is always possible to propose an event graph model which represents both the physical and the decision making systems. We use such a model to propose a near-optimal scheduling algorithm that maximizes productivity while minimizing the work-in-process (WIP) in the deterministic case. The approach used for non-cyclic manufacturing systems in the fourth section is different in the sense that only the manufacturing processes (i.e. the physical part of the system) and the related constraints are modeled using Petri nets. We use such a Petri net model to propose a short-term planning process which results in a trade-off between the computation burden and the level of resource utilization. The short-term planning model is then enhanced to obtain the scheduling model. The latter is used to develop an efficient scheduling algorithm that is able to satisfy the requirements imposed by short-term planning. Section five introduces a modular approach for modeling and managing manufacturing systems.


Output Transition Part Type Feasible Schedule Elementary Circuit Firing Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Jean-Marie Proth
    • 1
    • 2
  • Ioannis Minis
    • 2
  1. 1.Technopôle Metz 2000INRIA-LorraineMetzFrance
  2. 2.Institute for Systems ResearchUniversity of MarylandUSA

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