Multi-agent Coordination Mechanism of Virtual Supply Chain

2011 ◽  
pp. 620-629 ◽  
Author(s):  
Paulina Golinska ◽  
Marcin Hajdul
Keyword(s):  
Supply Chain ◽  
Coordination Mechanism ◽  
Multi Agent ◽  
Agent Coordination

Multi-Agent Applied Research in Agile Supply Chain

2013 ◽  
Vol 712-715 ◽  
pp. 3059-3062
Author(s):  
Jin Peng Tang ◽  
Ling Lin Li
Keyword(s):  
Supply Chain ◽  
System Reliability ◽  
Intelligent Agents ◽  
Applied Research ◽  
Coordination Mechanism ◽  
Supply Chain System ◽  
Agile Supply Chain ◽  
Multi Agent ◽  
Agent Coordination ◽  
The Competitiveness Of Enterprises

Introduced intelligent agents to agile supply chain, designed multi-agent coordination mechanism for agents, then proposed agile supply chain based on multi-agent system. This mechanism is applied to a specific enterprise. Multi-Agent strengthens the agile supply chain system reliability, flexibility and scalability, and improves the competitiveness of enterprises.

scholarly journals Research on the coordination mechanism of major industrial project engineering and construction multi-agents based on structural holes theory

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255858
Author(s):  
Xiaokang Han ◽  
Wenzhou Yan ◽  
Mei Lu
Keyword(s):  
Complex Network ◽  
Coordination Mechanism ◽  
Coordination Problem ◽  
Structural Holes ◽  
Network Diagram ◽  
Pmc Model ◽  
3D Network ◽  
Industrial Projects ◽  
Multi Agent ◽  
Agent Coordination

Industry is an important pillar of the national economy. Industrial projects are the most complex and difficult projects to control in the construction industry, and major industrial projects are even more complex and difficult to control. Multi-agent coordination is one of the core issues of industrial projects. Based on an analysis of the engineering and construction chains and agent relationships and agent networks of industrial projects, a complex network of the engineering and construction agents of industrial projects is established, and the complex network structural holes theory is applied to study the nonrepeated relationships among agents in industrial projects. Assuming agents are linked through contract relations and the most critical contract index between the agents in the contract amount, through structural hole analysis considering the EPC and PMC model, the aggregate constraint list is obtained, 2D network diagram and 3D network diagram are shown. According to the aggregate constraint value, the EPC contractor with the minimum aggregate constraint value and the project management company with the minimum aggregate constraint value are the critical agent in EPC and PMC model. By analyzing the complex network comprising different models of industrial projects, it is concluded that the characteristics of the agent maintain an advantage in competition, the coordination mechanism of the integration of agent interests, and multi-agent relations are considered to solve the multi-agent coordination problem in major industrial projects.

scholarly journals Improving Multi-agent Coordination by Learning to Estimate Contention

2021 ◽  
Author(s):  
Panayiotis Danassis ◽  
Florian Wiedemair ◽  
Boi Faltings
Keyword(s):  
Large Scale ◽  
Learning Algorithm ◽  
Coordination Mechanism ◽  
Large Scale Systems ◽  
Fast Learning ◽  
Agent Learning ◽  
Meeting Scheduling ◽  
Multi Agent ◽  
Device Deployment ◽  
Agent Coordination

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need for mutually consistent actions) by relying on the ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning is decentralized, observes only own action/reward pairs, requires no inter-agent communication, and achieves near-optimal (<5% loss) and fair coordination in a variety of synthetic scenarios and a real-world meeting scheduling problem. The lightweight nature and fast learning constitute ALMA-Learning ideal for on-device deployment.

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