Approximation of Pareto optima in multiple-objective, shortest-path problems
Operations Research
Computationally Manageable Combinational Auctions
Management Science
SIAM Journal on Computing
An interactive bi-objective shortest path approach: searching for unsupported nondominated solutions
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Integer Programming for Combinatorial Auction Winner Determination
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Applying learning algorithms to preference elicitation
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Combinatorial Auctions
Generating k-best solutions to auction winner determination problems
ACM SIGecom Exchanges
Eliciting bid taker non-price preferences in (combinatorial) auctions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
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Procurement executives often find it difficult to articulate their preferences and constraints regarding auctions, making it difficult to cast procurement decisions as straightforward optimization problems. This paper presents an efficient algorithm to aid decision support in such situations. Instead of trying to compute a single optimal solution for the auction winner determination problem, we generate many candidate solutions in ascending order of buyer expenditure. Standard techniques such as clustering and dominance pruning can then trim this list to a compact yet diverse menu of alternatives; other analyses can illuminate the cost of constraints and the competitive landscape. Our efficient solution-generation algorithm addresses sealed-bid procurement auctions with multiple suppliers and multiple types of goods available in multiple units. It supports multi-sourcing and volume discounts/surcharges in bids. Our algorithm may optionally incorporate certain classes of hard constraints, generating only solutions that satisfy them.