Decision support for truckload carriers in one-shot combinatorial auctions

Combinatorial auctions have become popular for shippers to secure transportation services. It is, however, very difficult for truck carriers to solve bid generation and evaluation problems in combinatorial auctions. The objective of this paper is to develop a bidding advisor to help truckload (TL) carriers overcome such challenging problems in one-shot combinatorial auctions. The proposed advisor integrates the load information in e-marketplaces with carriers' current fleet management plans, and then chooses the desirable load bundles. In this paper, a TL carrier's bid generation and evaluation problems in one-shot combinatorial auctions are formulated as a synergetic minimum cost flow problem by estimating the average synergy values between loads through the proposed approximation. The conventional solution approaches for solving the minimum cost flow problems cannot be applied to the synergetic network flow problem. Thus, we propose a column generation approach to solve this specific network flow problem. The main contribution of this paper is that a TL carrier adopting the proposed advisor can easily determine the desirable bid packages without evaluating all 2n-1 possible bundles of loads, where n is the number of loads.