An agent-based framework for collaborative negotiation in the global manufacturing supply chain network

2006 ◽  
Vol 22 (3) ◽  
pp. 239-255 ◽  
Author(s):  
Jianxin (Roger) Jiao ◽  
Xiao You ◽  
Arun Kumar
2008 ◽  
pp. 2598-2617
Author(s):  
Jianxin Jiao ◽  
Xiao You ◽  
Arun Kumar

This chapter applies the multi-agent system paradigm to collaborative negotiation in a global manufacturing supply chain network. Multi-agent computational environments are suitable for dealing with a broad class of coordination and negotiation issues involving multiple autonomous or semi-autonomous problem-solving agents. An agent-based multi-contract negotiation system is proposed for global manufacturingsupply chain coordination. Also reported is a case study of mobile phone global manufacturing supply chain management.


Author(s):  
Jianxin Jiao ◽  
Xiao You ◽  
Arun Kumar

This chapter applies the multi-agent system paradigm to collaborative negotiation in a global manufacturing supply chain network. Multi-agent computational environments are suitable for dealing with a broad class of coordination and negotiation issues involving multiple autonomous or semi-autonomous problem-solving agents. An agent-based multi-contract negotiation system is proposed for global manufacturingsupply chain coordination. Also reported is a case study of mobile phone global manufacturing supply chain management.


2010 ◽  
Vol 136 ◽  
pp. 82-85
Author(s):  
Rui Wang

This paper applies the multi-agent system paradigm to collaborative negotiation in supply chain network. Multi-agent computational environments are suitable for dealing with a class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving agents. An evolution teamwork system based on multi-agents that can organize most team members in supply chain network was proposed. The proposed model performs adaptive development relying on differential evolution process. The experimental results show that our developing teamwork system is able to provide the adaptability of team differential evolution is global optimization and continuously develop teamwork members for the resources management in supply chain network.


2019 ◽  
Vol 15 (2) ◽  
pp. 54-68 ◽  
Author(s):  
Jian Tan ◽  
Guoqiang Jiang ◽  
Zuogong Wang

In the supply chain network, information sharing between enterprises can produce synergistic effect and improve the benefits. In this article, evolutionary game theory is used to analyse the evolution process of the information sharing behaviour between supply chain network enterprises with different penalties and information sharing risk costs. Analysis and agent-based simulation results show that when the amount of information between enterprises in supply chain networks is very large, it is difficult to form a sharing of cooperation; increase penalties, control cost sharing risk can increase the probability of supply chain information sharing network and shorten the time for information sharing.


2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Tengjiao Li ◽  
Hongzhuan Chen ◽  
Jie Yuan ◽  
Jingye Qian ◽  
Abdul Waheed Siyal

The collaborative development of complex products has gradually developed into a “main manufacturer-suppliers” mode, under which the manufacturing enterprises form a complex product collaborative manufacturing supply chain network. Quality risks which bring enormous hidden danger to the product quality can be propagated and accumulate along the supply chain. It is of great significance to quantify the propagation mechanism of quality risk between supply chain network nodes and identify the key quality risk factor that causes fluctuation of product quality. This study for the first time applies the SoV into the research on quality risk propagation of complex product collaborative manufacturing supply chain network. Firstly, this paper uses the CN to construct a complex product collaborative manufacturing supply chain network according to its characteristics. Secondly, on the basis of SoV, the quality risk propagation model is established. Thirdly, we put forward a method to identify the key quality risk factors of supply chain network based on the risk propagation effect. Lastly, a numerical simulation is given to verify the effectiveness of the model and its identification method. The results reveal that the quality risk propagation includes the vertical propagation within enterprises and the horizontal propagation from the lower-level enterprises to the upper-level enterprises of the supply chain. The quality risks of an enterprise are determined by its own quality risk factors and the quality risk passed by the lower-level enterprises.


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