Control point policy optimization using genetic algorithms

Abstract

This paper describes the application of an integrated Genetic Algorithm (GA)/Discrete Event Simulation model for selecting optimum values for Critical Point Policy (CPP) hedging time and buffer size parameters. The CPP is shown to perform well, when compared with the Critical Ratio priority rule, in terms of improving service levels, particularly when subject to conditions where buffer sizes and Takt times are required to be small. The technique developed involves buffer sizes being chosen by a GA according to a constraint on the total storage space available within the system. A method is described for reducing the number of variables that the GA needs to deal with, hence, improving the efficiency of the GA optimization process. The development and application work reported also provides further understanding into how and when the CPP should be applied.

Keywords:

• Control point policy
• Genetic algorithms
• Critical ratio
• Hedging time
• Buffer size
• Simulation

Acknowledgements

The authors wish to thank the UK's Engineering and Physical Science Council for sponsoring this research work through its Innovative Manufacturing Initiative (EPSRC Grant No. GR/M58818).