A faster optimal allocation algorithm in combinatorial auctions

In combinatorial auctions, a bidder may bid for arbitrary combinations of items, so combinatorial auction can be applied to resource and task allocations in multiagent systems. But determining the winners of combinatorial auctions who maximize the profit of the auctioneer is known to be NP-complete. A branch-and-bound method can be one of efficient methods for the winner determination. In this paper, we propose a faster winner determination algorithm in combinatorial auctions. The proposed algorithm uses both a branch-and-bound method and Linear Programming. We present a new heuristic bid selection method for the algorithm. In addition, the upper-bounds are reused to reduce the running time of the algorithm in some specific cases. We evaluate the performance of the proposed algorithm by comparing with those of CPLEX and a known method. The experiments have been conducted with six datasets each of which has a different distribution. The proposed algorithm has shown superior efficiency in three datasets and similar efficiency in the rest of the datasets.