Paper Info
Title
Meta-heuristic Enabled MAS Optimization in Supply Chain Procurement
Abstract
This paper introduces a meta-heuristic enabled multi-agent optimization architecture for dynamic transportation planning in the supply chain procurement (SCP) plans. When multi-agent systems (MAS) are used for real-time dynamic optimization, agents seek the solution using distributed heuristics. However, distributed heuristics based on local information are prone to converge at local optimality. To escape from local optimality toward higher quality solution, we introduce meta-heuristics over agent interactions to advise agents' searching process. In this paper, we mainly propose variable neighborhood search meta-heuristic (VNS-MH) over distributed market based heuristic (DMBH), a distributed heuristic based on market interactions for transportation planning. The numerical results show that VNS-MH performs better on achieving optimality than DMBH.
Year
DOI
Venue
2008
10.1109/SNPD.2008.84
SNPD
Keywords
Field
DocType
variable neighborhood search meta-heuristic,higher quality solution,multi-agent optimization architecture,local information,multi-agent system,transportation planning,supply chain procurement,meta-heuristic enabled mas optimization,market interaction,local optimality,real-time dynamic optimization,dynamic transportation planning,transportation,multi agent systems,procurement,supply chain,optimization,routing,planning,multi agent system,supply chains
Heuristic,Mathematical optimization,Architecture,Variable neighborhood search,Computer science,Multi-agent system,Heuristics,Supply chain,Procurement,Transportation planning,Distributed computing
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Xia Zhanguo111.09
Wang Ke272.62
Wang Zhixiao300.34