Coordinating Multi-Attribute Reverse Auctions Subject to Temporal and Capacity Constraints

Over the past few years, reverse auctions have attracted a lot of attention in the AI community. They offer the prospect of more efficiently matching suppliers and producers in the face of changing market conditions. Prior research has generally ignored the temporal and finite capacity constraints under which reverse auctioneers typi-cally operate. In this paper, we consider the problem faced by a reverse auctioneer (e.g. a manufacturer) that can procure key components or services from a number of possible suppliers through multi-attribute reverse auctions. This problem can also be viewed as a static abstraction of the procurement problem faced by agents in the new TAC’03 Supply Chain Trading Competition. Bids submitted by prospective suppliers include a price and a delivery date. The reverse auctioneer has to select a combination of supplier bids that will maximize its overall profit, taking into ac-count its own finite capacity and the prices and delivery dates offered by different suppliers for the same components/services. The auctioneer’s profit is determined by the revenue generated by the products it sells, the costs of the compo-nents/services it purchases as well as late delivery penalties it incurs if it fails to deliver prod-ucts/services in time to its own customers. We provide a formal model of this important class of problems, discuss its complexity and introduce rules that can be used to efficiently prune the resulting search space. We also introduce a branch-and-bound algorithm and an efficient heuristic search procedure for this class of prob-lems. Empirical results show that our heuristic procedure typically yields solutions that are within 10 percent of the optimum. They also in-dicate that taking into account finite capacity considerations can significantly improve the re-verse auctioneer’s bottom line.