Multi-Attribute Supply Chain Negotiation: Coordinating Reverse Auctions Subject to Finite Capacity Considerations
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Abstract or Description
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.