Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Solving Combinatorial Auctions Using Stochastic Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Using a Lagrangian Heuristic for a Combinatorial Auction Problem
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Taming the computational complexity of combinatorial auctions: optimal and approximate approaches
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
CABOB: a fast optimal algorithm for combinatorial auctions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Combinatorial reverse auction based on revelation of Lagrangian multipliers
Decision Support Systems
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Although combinatorial auction has been studied extensively, it is difficult to apply the existing results to a problem with minimal resource requirements. In this paper, we consider a combinatorial auction problem in which an auctioneer wants to acquire resources from a set of bidders to process the tasks on hand. Each task requires a minimal set of resources for executing the operations. Each bidder owns a set of resources to bid for the tasks. The problem is to determine the resource assignment to minimize the total cost to perform the tasks. The main results include: (1) a problem formulation for combinatorial auction with minimal resource requirements; (2) a solution methodology based on Lagrangian relaxation; (3) an economic interpretation and a proposed structure for implementing our solution algorithms.