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

被引：45

作者：

Zarandi, M. H. Fazel ^{[2
]}

Pourakbar, M. ^{[2
]}

Turksen, I. B. ^{[1
,3
]}

机构：

[1] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON M5S 3G8, Canada

[2] Amir Kabir Univ Technol, Dept Ind Engn, Tehran, Iran

[3] TOBB Econ & Technol Univ, Dept Ind Engn, TR-06560 Ankara, Turkey

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2008年 / 34卷 / 03期

关键词：

supply chain management; agent-based; fuzzy time series; bullwhip effect; simulation;

D O I：

10.1016/j.eswa.2007.01.031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：1680 / 1691

页数：12

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