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A collaborative decision-making approach for supply chain based on a multi-agent system

Abstract : To improve the supply chain's performance under demand uncertainty and exceptions, various levels of collaboration techniques based on information sharing were set up in real supply chains (VMI, CPR, CPFR...). The main principle of these methods is that the retailers do not need to place orders because wholesalers use information centralization to decide when to replenish them. Although these techniques could be extended to a whole supply chain, current implementations only work between two business partners. With these techniques, companies electronically exchange a series of written comments and supporting data, which includes past sales trends, scheduled promotions, and forecasts. This allows participants to coordinate joint forecasting by focusing on differences in forecasts. But if the supply chain consists of autonomous enterprises, sharing information becomes a critical obstacle, since each independent actor is typically not willing to share with the other nodes its own strategic data (as inventory levels); That is why researchers proposed different methods and information systems to let the members of the supply chain collaborate without sharing all their confidential data and information. In this chapter we analyze some of the existing approaches and works and describe an agent-based distributed architecture for the decision-making process. The agents in this architecture use a set of negotiation protocols (such as Firm Heuristic, Recursive Heuristic, CPFR Negotiation Protocol) to collectively make decisions in a short time. The architecture has been validated on an industrial case study.
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Contributor : Yacine Ouzrout <>
Submitted on : Tuesday, October 30, 2018 - 3:28:02 PM
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  • HAL Id : hal-00442392, version 1


Yacine Ouzrout, Abdelaziz Bouras, El Habib Nfaoui, Omar El Beqqali. A collaborative decision-making approach for supply chain based on a multi-agent system. Benyoucef, Lyes; Grabot, Bernard (Eds.). Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management, Springer, pp.520, 2010, Springer Series in Advanced Manufacturing. ⟨hal-00442392⟩



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