A PSO Weighted Estimating the Mean Downstream Demand to Mitigate the Bullwhip Effect in a Supply Chain

Abstract:

Bullwhip Effect, Particle Swarm Optimization, Supply Chain, Demand Information Abstract. A deformation phenomenon occurring in business activity, called the bullwhip effect which comes from the demand information is not fully shared among the members of a supply chain, conducts the upstream manufacturer to excessively anticipate the demand capacity of the downstream retailer. The manufacturer improperly decides the amount of the products not only to raise the inventory cost on the way of poorly handling the actually downstream demand, but also to lose the chance of business deals due to its backordering. To cope with the bullwhip effect by taking into account the holding and backorder costs, an evolutionary method based on the Particle Swarm Optimization (PSO) algorithm to estimate the critical parameter, mean downstream demand, is proposed and computer validated in this paper such that the estimated inventory level could be close the really batch ordering of the manufacturer.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 4125-4129
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.4125
• Online since: November 2011
• Authors: Ling Tzu Tseng
• Keywords: Bullwhip Effect, Demand Information, Particle Swarm Optimization Algorithm (PSO), Supply Chain

References

[1] P. H. Lin , D. S. H. Wong, S. S. Jang, S. S. Shieh and J. Z Chu: Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis (Journal of Process Control, Vol. 14, No. 5, pp.487-2004).

DOI: 10.1016/j.jprocont.2003.09.005

[2] J. S. R. Daniel and C. Rajendran: A simulation-based genetic algorithm for inventory optimization in a serial supply chain, Vol. 12, No. 1, pp.101-127 (International Transactions in Operational Research 2005).

DOI: 10.1111/j.1475-3995.2005.00492.x

[3] C. F. Chen: The relationship between the cost of supply chain and bullwhip considering the case of variable lead time, Master Thesis, National Cheng Kung University (2003).

[4] W. P. Huang, L. F. Zhou and J. X. Qian: FIR filter design: Frequency sampling filters by particle swarm optimization algorithm, pp.2322-2327 (Proceedings of the IEEE 3rd International Conference on Machine Learning and Cybernetics 2004).

DOI: 10.1109/icmlc.2004.1382187

[5] J. Kennedy and R. Eberhart: Particle swarm optimization, pp.1942-1948 (Proceedings of the IEEE International Conference on Neural Networks 1995).