A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems

Authors:
M.H. Fazel Zarandi;M. Pourakbar;I. B. Turksen
Affiliations:
Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran;Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran;Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ont., Canada M5S 3G8 and Department of Industrial Engineering, TOBB Economy and Technology University, Sogutozo ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This paper addresses the bullwhip effect in a multi-stage supply chain, where all demands, lead times, and ordering quantities are fuzzy. To simulate the bullwhip effect, a modified Hong Fuzzy Time Series is presented by adding a Genetic Algorithm (GA) module for gaining of a window basis. Next, a back propagation neural network is used for defuzzification. The model can forecast the trends in fuzzy data. Then, an agent-based system is developed to minimize the total cost and to reduce the bullwhip effect in supply chains. The system can suggest the reasonable ordering policies. The results show that the propose system is superior than the previous analytical methods in terms of discovering the best available ordering policies.