Combinatorial auctions for supply chain formation
Proceedings of the 2nd ACM conference on Electronic commerce
Market protocols for decentralized supply chain formation
Market protocols for decentralized supply chain formation
Allocating tasks in extreme teams
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A Multi-Agent Negotiation Testbed for Contracting Tasks with Temporal and Precedence Constraints
International Journal of Electronic Commerce
Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Anytime optimal coalition structure generation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Decentralized supply chain formation: a market protocol and competitive equilibrium analysis
Journal of Artificial Intelligence Research
Bidding languages and winner determination for mixed multi-unit combinatorial auctions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Near-optimal anytime coalition structure generation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Coalition formation with spatial and temporal constraints
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3
CHAINME: fast decentralized finding of better supply chains
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Supply Chain Formation is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized supply chain formation appears as a highly intricate task because agents only possess local information and have limited knowledge about the capabilities of other agents. The decentralized supply chain formation problem has been recently cast as an optimization problem that can be efficiently approximated using max-sum loopy belief propagation. This mapping can be improved by encoding the problem into a binary factor graph (containing only binary variables) and deriving model-specific equations for max-sum. First, this paper introduces the state-of-the art methods for decentralized supply chain formation. Second, it presents future short-term lines of research in this problem. Finally, it argues that the binary model can be extended to other problems than that of the supply chain formation.