Computationally Manageable Combinational Auctions
Management Science
Truth revelation in approximately efficient combinatorial auctions
Proceedings of the 1st ACM conference on Electronic commerce
Approaches to winner determination in combinatorial auctions
Decision Support Systems - Special issue on information and computational economics
Computationally feasible VCG mechanisms
Proceedings of the 2nd ACM conference on Electronic commerce
Combinatorial auctions for supply chain formation
Proceedings of the 2nd ACM conference on Electronic commerce
Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Auctioning and bidding in electronic commerce: the online auction
Auctioning and bidding in electronic commerce: the online auction
Iterative combinatorial auctions: achieving economic and computational efficiency
Iterative combinatorial auctions: achieving economic and computational efficiency
Economic mechanism design for computerized agents
WOEC'95 Proceedings of the 1st conference on USENIX Workshop on Electronic Commerce - Volume 1
An Agent-Based Dynamic Information Network for Supply Chain Management
BT Technology Journal
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In this paper, we propose a procurement mechanism for selecting optimal supplier combination when the suppliers have interoperability and cost dependency issue. The proposed mechanism, named Multi-Component Contingent Auction(MCCA), guarantees that the outcome is the combination of the suppliers with the minimum total cost who can work together. The core of the MCCA is a special form of bid: contingent bid. Contingent bids provide the suppliers with a way to express incompatibility and cost dependency. Suppliers can also implement a package bid by submitting multiple contingent bids. Therefore, the MCCA can be considered as a super set of the combinatorial auction.A major problem in the MCCA is the number of computations required for winner determination. We propose a winner determination algorithm that alleviates this computational burden and compare its performance with the depth-first tree search algorithm.