Interfaces - Special issue: Franz Edelman award for achievement in operations research and the management sciences
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
The Impact of the Secondary Market on the Supply Chain
Management Science
Iterative Dutch combinatorial auctions
Annals of Mathematics and Artificial Intelligence
Decision support for multi-unit combinatorial bundle auctions
Decision Support Systems
Efficient Auction Mechanisms for Supply Chain Procurement
Management Science
Buyer's Efficient E-Sourcing Structure: Centralize or Decentralize?
Journal of Management Information Systems
Solving a sealed-bid reverse auction problem by multiple-criterion decision-making methods
Computers & Mathematics with Applications
Truthful Bundle/Multiunit Double Auctions
Management Science
Combinatorial reverse auction based on revelation of Lagrangian multipliers
Decision Support Systems
Borrower Decision Aid for people-to-people lending
Decision Support Systems
Mechanism Design for Decentralized Online Machine Scheduling
Operations Research
Research Commentary---Designing Smart Markets
Information Systems Research
A Laboratory Investigation of Rank Feedback in Procurement Auctions
Manufacturing & Service Operations Management
| Hi-index | 0.01 |
We address the problem of designing multi-item procurement auctions for a monopsonistic buyer in capacity-constrained environments. Using insights from classical auction theory, we construct an optimization-based auction mechanism ("smart market") relying on the dynamic resolution of a linear program minimizing the buyer's cost under the suppliers' capacity constraints. Suppliers can modify their offers in response to the optimal allocation corresponding to each set of bids, giving rise to a dynamic competitive bidding process. To assist suppliers, we also develop a bidding-suggestion device based on a myopic best-response (MBR) calculation that solves an associated optimization problem. Assuming linear costs for the suppliers, we study within a game-theoretic framework the sequence of bids arising in this smart market. Under a weak behavioral assumption and some symmetry requirements, an explicit upper bound for the winning bids is established. We then formulate a complete behavioral model and solution methodology based on the MBR rationale and show that the bounds derived earlier continue to hold. We analytically derive some structural and convergence properties of the MBR dynamics in the simplest nontrivial market environment, which suggests further possible design improvements, and investigate bidding dynamics and incentive compatibility issues via numerical simulations.