Using Artificial Neural Network in Multi-Agent Supply Chain Systems

In modern global market, one of the most important issues of the supply chain (SC) management is to satisfy changing customer demands, and enterprises should enhance the long-term advantage through the optimal inventory control. In this study, we model a supply chain framework by multi-agent with mi...

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Bibliographic Details
Published inThird International Conference on Natural Computation (ICNC 2007) Vol. 1; pp. 348 - 352
Main Authors Hsiao Ching Chen, Hui Ming Wee, Kung-Jeng Wang, Yao-Hung Hsieh
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Artificial neural networks
Costs
Inventory control
Inventory management
Mixed Inventory
Multi-Agent Systems
Multiagent systems
Neural Networks
Policy
Production facilities
Production systems
Robustness
Supply Chain
Supply chain management
Supply chains
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Summary:In modern global market, one of the most important issues of the supply chain (SC) management is to satisfy changing customer demands, and enterprises should enhance the long-term advantage through the optimal inventory control. In this study, we model a supply chain framework by multi-agent with mixed inventory policies of facilities to consider the impact factors of the total supply chain cost. This paper develops a multi-agent system to simulate supply chain system. Artificial Neural Network (ANN) is used to derive the optimal inventory policies in the SC numbers. We examine the performance of the optimal inventory policies by cutting costs and increasing supply chain management efficiency. The proposed inventory policy using multi-agent and ANN provides managerial insights on the impact of the decision making in all the SC numbers.
ISBN:9780769528755
0769528759
ISSN:2157-9555
DOI:10.1109/ICNC.2007.798