E-Business and Management Science: Mutual Impacts (Part 1 of 2)
Management Science
MoRVAM: A reverse Vickrey auction system for mobile commerce
Expert Systems with Applications: An International Journal
Models for Iterative Multiattribute Procurement Auctions
Management Science
Efficient Auction Mechanisms for Supply Chain Procurement
Management Science
Procuring Fast Delivery: Sole Sourcing with Information Asymmetry
Management Science
Generalized value decomposition and structured multiattribute auctions
Proceedings of the 8th ACM conference on Electronic commerce
Relationship preserving auction for repeated e-procurement
Proceedings of the 10th international conference on Electronic commerce
A unified framework for multiple criteria auction mechanisms
Web Intelligence and Agent Systems
Solving a sealed-bid reverse auction problem by multiple-criterion decision-making methods
Computers & Mathematics with Applications
Supply Disruptions, Asymmetric Information, and a Backup Production Option
Management Science
A framework for QoS-based Web service contracting
ACM Transactions on the Web (TWEB)
RFQ Auctions with Supplier Qualification Screening
Operations Research
Competition in the Supply Option Market
Operations Research
Improving efficiency in multiple-unit combinatorial auctions: Bundling bids from multiple bidders
Decision Support Systems
Coordinated selection of procurement bids in finite capacity environments
Electronic Commerce Research and Applications
A secure reverse Vickrey auction scheme with bid privacy
Information Sciences: an International Journal
Optimal procurement contract between contractor and supplier in large-scale projects
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Mechanism Design for Decentralized Online Machine Scheduling
Operations Research
Information revelation in multiattribute English auctions: A laboratory study
Decision Support Systems
Multiattribute auctions based on generalized additive independence
Journal of Artificial Intelligence Research
A fuzzy TOPSIS based approach for e-sourcing
Engineering Applications of Artificial Intelligence
Preference-based English reverse auctions
Artificial Intelligence
An efficient reverse auction mechanism for limited supplier base
Electronic Commerce Research and Applications
Optimal Procurement Design in the Presence of Supply Risk
Manufacturing & Service Operations Management
A public procurement combinatorial auction mechanism with quality assignment
Decision Support Systems
Heuristic algorithms for the inverse mixed integer linear programming problem
Journal of Global Optimization
A review of conventional and knowledge based systems for machining price quotation
Journal of Intelligent Manufacturing
Design of online auctions: Proxy versus non-proxy settings
Decision Support Systems
Winner determination based on preference elicitation methods
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Manufacturing & Service Operations Management
Inverse conic programming with applications
Operations Research Letters
Procurement decision making mechanism of divisible goods based on multi-attribute auction
Electronic Commerce Research and Applications
Computers and Industrial Engineering
Branch-and-bound algorithms for the partial inverse mixed integer linear programming problem
Journal of Global Optimization
Journal of Management Information Systems
NegotiAuction: An experimental study
Decision Support Systems
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We consider a manufacturer who uses a reverse, or procurement, auction to determine which supplier will be awarded a contract. Each bid consists of a price and a set of nonprice attributes (e.g., quality, lead time). The manufacturer is assumed to know the parametric form of the suppliers' cost functions (in terms of the nonprice attributes), but has no prior information on the parameter values. We construct a multiround open-ascending auction mechanism, where the manufacturer announces a slightly different scoring rule (i.e., a function that ranks the bids in terms of the price and nonprice attributes) in each round. Via inverse optimization, the manufacturer uses the bids from the first several rounds to learn the suppliers' cost functions, and then in the final round chooses a scoring rule that attempts to maximize his own utility. Under the assumption that suppliers submit their myopic best-response bids in the last round, and do not distort their bids in the earlier rounds (i.e., they choose their minimum-cost bid to achieve any given score), our mechanism, indeed, maximizes the manufacturer's utility within the open-ascending format. We also discuss several enhancements that improve the robustness of our mechanism with respect to the model's informational and behavioral assumptions.