Scheduling of discrete event systems using mixed integer linear programming

To remain competitive, the modern industry strive for flexibility. Recently, a method for automatic generation of control code from a 3D simulation model of a flexible manufacturing system was developed. Finite automata and supervisory control theory (SCT) were used to guarantee the required behaviour of the system. This paper moves one step further. A method for automatic conversion between deterministic finite automata and mixed integer linear programming (MILP) formulation is presented. This allows to efficiently combine SCT and MILP to automatically generate time-optimal, collision-free and non-blocking working schedules

[1]  W. M. Wonham,et al.  The control of discrete event systems , 1989 .

[2]  Alexandre M. Bayen,et al.  MILP formulation and polynomial time algorithm for an aircraft scheduling problem , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[3]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[4]  W. Wonham,et al.  Control of vector discrete-event systems. II. Controller synthesis , 1994, IEEE Trans. Autom. Control..

[5]  Colin R. Reeves,et al.  Genetic Algorithms: Principles and Perspectives: A Guide to Ga Theory , 2002 .

[6]  Ted K. Ralphs,et al.  Noncommercial Software for Mixed-Integer Linear Programming , 2005 .

[7]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[8]  Srinivas Akella,et al.  Coordinating Multiple Double Integrator Robots on a Roadmap: Convexity and Global Optimality , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[9]  Jaime Cerdá,et al.  An MILP framework for batch reactive scheduling with limited discrete resources , 2004, Comput. Chem. Eng..

[10]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[11]  Chanwoo Moon,et al.  PLC based coordination schemes for a multi-robot system , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[12]  Knut Åkesson,et al.  Automatic model generation and PLC-code implementation for interlocking policies in industrial robot cells , 2007 .

[13]  Martin Fabian,et al.  Scheduling algorithms for optimal robot cell coordination - a comparison , 2006, 2006 IEEE International Conference on Automation Science and Engineering.

[14]  R. Malik,et al.  Supremica - An integrated environment for verification, synthesis and simulation of discrete event systems , 2006, 2006 8th International Workshop on Discrete Event Systems.

[15]  Jiyin Liu,et al.  A global MILP model for FMS scheduling , 1997, Eur. J. Oper. Res..