Batch sizes optimisation by means of queueing network decomposition and genetic algorithm

Abstract

Batch sizes have a considerable impact on the performance of a manufacturing process. Determining optimal values for batch sizes helps to reduce inventories/costs and lead times. The deterministic nature of the available batch size optimisation models reduces the practical value of the obtained solutions. Other models focus only on critical parts of the system (e.g., the bottleneck). In this paper, we present an approach that overcomes important limitations of such simplified solutions. We describe a combination of queueing network analysis and a genetic algorithm that allows us to take into account the real characteristics of the system when benefiting from an efficient optimisation mechanism. We are able to demonstrate that the application of our approach on a real-sized problem with 49 products allows us to obtain a solution (values for batch sizes) with less than 4% relative deviation of the cycle time from the exact minimal value.

Keywords:

• optimisation
• batch sizing
• queueing networks
• genetic algorithms
• manufacturing systems
• decomposition

Acknowledgements

This work is supported by the Seventh Framework Programme – The People Programme – Marie Curie Industry–Academia Partnerships and Pathways Project (No. 217891) ‘Keeping Jobs in Europe’.