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Application Of Multi-agent Reinforcement Learning To Supply Chain Ordering Management

G. Zhao, R. Sun
Published 2010 · Computer Science

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Coordination in the Supply Chain Management (SCM) plays a major role in competitive advantages in today's uncertain business environments. There is strong evidence of success in the supply chain performance in cases with high coordination among echelons. The bullwhip effect is an important phenomenon of amplification of demand in supply chain. By eliminating the bullwhip effect, it is possible to increase product profitability. Reinforcement Learning (RL) is successfully applied to some dynamical and unpredictable issues. By surveying some efficient multi-agent RL mechanisms, this paper presents a multi-layer ordering control strategy by a multi-agent coordination model with RL mechanism for alleviating, effectively and efficiently, the bullwhip effect of a supply chain with multiple members and multi commodities. As an improvement to previous works using RL in supply chain ordering management domain, the method presented in this paper can be used to deal with multiple members with multi commodities in each echelon and each player. As a result, the methodology presented in this study is expected to help address issues regarding the uncertainty and complexity of deriving the maximal profit using RL technique in the stochastic supply chain with multiple echelons and multiple members with multiple commodities among supply chain members for efficient supply chain coordination.
This paper references
10.1109/IEEM.2007.4419448
Employing genetic algorithms to minimise the bullwhip effect in a supply chain
J. Lu (2007)
10.1109/TNN.1998.712192
Reinforcement Learning: An Introduction
R. Sutton (2005)
10.1057/palgrave.jors.2600946
Industrial Dynamics
J. Forrester (1997)
10.1023/A:1022633531479
Learning to Predict by the Methods of Temporal Differences
R. Sutton (2005)
10.1016/J.IJPE.2007.10.009
A supply chain as a series of filters or amplifiers of the bullwhip effect
G. Caloiero (2008)
10.1023/A:1022676722315
Technical Note: Q-Learning
Christopher J.C.H. Watkins (2004)
10.1109/IEEM.2007.4419513
A simulation model of bullwhip effect in a multi-stage supply chain
P. Wangphanich (2007)
10.1016/S0167-9236(02)00019-2
Computers play the beer game: can artificial agents manage supply chains?
S. Kimbrough (2002)
10.1109/PACIIA.2009.5406476
Exploring the bullwhip effect through supply chain simulation software
Li Zhuo-qun (2009)
10.1016/j.eswa.2008.06.082
A multi-echelon inventory management framework for stochastic and fuzzy supply chains
Alev Taskin Gumus (2009)
10.1109/IEEM.2008.4738197
A simulation study on the impact of forecasting methods on the bullwhip effect in the supply chain
S. K. Chaharsooghi (2008)
10.1109/INDIN.2006.275663
Policy Transition of Reinforcement Learning for an Agent Based SCM System
Gang Zhao (2006)
10.4018/978-1-59140-929-8.CH003
Information Sharing in Supply Chain Management with Demand Uncertainty
Z. Huang (2006)
10.1613/jair.301
Reinforcement Learning: A Survey
L. Kaelbling (1996)
10.1016/J.IJPE.2007.10.022
Mitigating the bullwhip effect by ordering policies and forecasting methods
D. Wright (2008)
10.1287/opre.49.2.226.13528
Optimal Policies for Multiechelon Inventory Problems with Markov-Modulated Demand
Fangruo Chen (2001)
10.1016/J.JOM.2004.08.007
Information sharing and coordination in make-to-order supply chains
F. Sahin (2005)
Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems
Satinder Singh (1996)
Designing and Evaluating an Adaptive Trading Agent for Supply Chain Management Applications
M. He (2005)
A Reinforcement Learning Approach to job-shop Scheduling
Wei Zhang (1995)
10.1016/b978-1-55860-141-3.50030-4
Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming
R. Sutton (1990)
10.1080/00207540500431347
Minimizing the bullwhip effect in a supply chain using genetic algorithms
T. O'Donnell (2006)
10.1109/ICSMC.2002.1168009
Application of multiagent reinforcement learning - to multicast routing in wireless ad hoc networks ensuring resource reservation
R. Sun (2002)
10.1109/IEEM.2009.5373069
A Genetic Algorithm approach to reducing the Bullwhip Effect by investigating the efficient and responsive strategy in online supply chains
J. Lu (2009)
A Reinforcement Learning Approach for Supply Chain Management
Tim Stockheim
10.1016/j.dss.2008.03.007
A reinforcement learning model for supply chain ordering management: An application to the beer game
S. K. Chaharsooghi (2008)
10.1016/S0925-5273(00)00156-0
Inventory management in supply chains: a reinforcement learning approach
I. Giannoccaro (2002)
10.1287/mnsc.1040.0265
Optimal Policies for a Multi-Echelon Inventory Problem
A. Clark (2004)



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