An Intelligent Agent-based System for Reduction of Bullwhip Effect in Supply Chains

Abstract

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