posted on 2003-01-01, 00:00authored byJiong Sun, Norman Sadeh
Reverse auctions 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
typically operate. In this paper, we consider the problem faced by a manufacturer (or
service provider) that needs to fulfill a number of customer orders, each requiring a
possibly different combination of components (or services). The manufacturer can
procure these components or services from a number of possible suppliers through
multi-attribute reverse auctions. 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 account its own finite
capacity and the prices and delivery dates offered by different suppliers for the same
components/services. The manufacturer’s profit is determined by the revenue
generated by the products it sells, the cost of the components/services it purchases, as
well as late delivery penalties it incurs if it fails to deliver products/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 that takes
advantage of these pruning rules along with two heuristic search procedures.
Computational results are presented that evaluate the performance of our heuristic
procedures under different conditions both in terms of computational requirements
and distance from the optimum. Our experiments show that taking into account finite
capacity considerations can significantly improve the manufacturer’s bottom line,
thereby confirming the importance of these constraints and the effectiveness of our
search heuristics.