An Adaptive Genetic Algorithm based approach for production reactive scheduling of manufacturing systems

The problem for scheduling the manufacturing systems production involves the system modeling task and the application of a technique to solve it. There are several ways used to model the scheduling problem and search strategies have been applied on the models to find a solution. The solutions consider performance parameters like makespan. However, depending on the size and complexity of the system, the response time becomes critical, mostly when itpsilas necessary to reschedule. Researches aim to use Genetic Algorithms as a search method to solve the scheduling problem. This paper proposes the use of Adaptive Genetic Algorithm (AGA) to solve this problem having as performance criteria the minimum makespan and the response time. The probability of crossover and mutation is dynamically adjusted according to the individualpsilas fitness value. The proposed approach is compared with a traditional Genetic Algorithm (GA).

[1]  Raymond W. Yeung,et al.  Genetic algorithms and flexible process planning in the design of fault tolerant cell control for flexible assembly systems , 2000, Int. J. Comput. Integr. Manuf..

[2]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[3]  P. Pongcharoena,et al.  Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products , 2002 .

[4]  Yuehong Yin,et al.  A genetic algorithm based approach to flowshop scheduling , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[5]  E.R.R. Kato,et al.  A Heuristic based on Petri Nets modeling for FMS Scheduling problem of makespan minimization , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[6]  Ling Wang,et al.  An adaptive genetic algorithm with multiple operators for flowshop scheduling , 2006 .

[7]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[8]  Li-Chen Fu,et al.  A GA embedded dynamic search algorithm over a Petri net model for an FMS scheduling , 1997, Proceedings of International Conference on Robotics and Automation.

[9]  R. Saravanan,et al.  Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm , 2006 .

[10]  E.R.R. Kato,et al.  A search method using Genetic Algorithm for production reactive scheduling of manufacturing systems , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[11]  Salvatore Cavalieri.  Petri nets and genetic algorithms to increase productivity in FMS , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[12]  S. G. Ponnambalam,et al.  An intelligent integrated scheduling model for Flexible Manufacturing System , 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..

[13]  Pupong Pongcharoen,et al.  The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure , 2004, Eur. J. Oper. Res..