Simulation Based Scheduling Applying Petri Nets with Sequences and Priorities

The paper deals with modelling and simulation of scheduling and sequencing problems based on Petri nets. In particular, the Petri-net models are simulated over the time domain and a simulation-based optimisation is implemented to optimise the input sequences. A new conflict resolution is implemented and a sophisticated way of defining firing sequences is developed. This enables the optimisation of scheduling problems by automated changing and evaluating the used sequences. The optimisation problem is solved by heuristic algorithms, including genetic algorithms, simulated annealing and threshold accepting. All these methods are implemented in the so called MATLAB PetriSimM toolbox which offers the capability of modelling, simulation, and optimisation of Timed, Coloured, and Stochastic Petri nets. For comparison, a Petri net based heuristic search is implemented, which is based on the reachability tree exploration. The algorithms are compared and tested in a case study dealing with modelling, simulation and optimisation of a production cell.

[1]  S. Griffis.  EDITOR , 1997, Journal of Navigation.

[2]  Masahiko Fuyuki,et al.  A Simulation‐based Production Scheduling Method for Minimizing the Due‐date‐deviation , 2002 .

[3]  Peter Brucker,et al.  Scheduling Algorithms , 1995 .

[4]  Gerald Weigert,et al.  Optimization of manufacturing processes by distributed simulation , 2006 .

[5]  Frank DiCesare,et al.  Scheduling flexible manufacturing systems using Petri nets and heuristic search , 1994, IEEE Trans. Robotics Autom..

[6]  Manuel Silva Suárez,et al.  Petri Nets for the Design and Operation of Manufacturing Systems , 1997, Eur. J. Control.

[7]  Yoshikazu Nishikawa,et al.  Simulation-based scheduling package-models and solutions , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.

[8]  Miquel Angel Piera Eroles,et al.  Optimization of Logistic and Manufacturing Systems through Simulation: A Colored Petri Net-Based Methodology , 2004, Simul..

[9]  Hisashi Tamaki,et al.  Simulation-based scheduling package using generalized Petri net models , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.

[10]  Hongnian Yu,et al.  Combined Petri net modelling and AI-based heuristic hybrid search for flexible manufacturing systems-part I: Petri net modelling and heuristic search , 2003 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[13]  Sheik Meeran,et al.  Deterministic job-shop scheduling: Past, present and future , 1999, Eur. J. Oper. Res..

[14]  MengChu Zhou,et al.  Scheduling of semiconductor test facility via Petri nets and hybrid heuristic search , 1998 .

[15]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[16]  Jan Ola Strandhagen,et al.  Applications of Discrete-Event Simulation to Support Manufacturing Logistics Decision-Making: A Survey , 2006, Proceedings of the 2006 Winter Simulation Conference.

[17]  R. Vidal.  Applied simulated annealing , 1993 .

[18]  Hongnian Yu,et al.  Combined Petri net modelling and AI-based heuristic hybrid search for flexible manufacturing systems-part II: heuristic hybrid search , 2003 .

[19]  Gasper Music,et al.  Production-process modelling based on production-management data: a Petri-net approach , 2007, Int. J. Comput. Integr. Manuf..