skip to main content
article

Generating k-best solutions to auction winner determination problems

Published: 01 June 2006 Publication History

Abstract

Auction participants cannot always articulate their requirements and preferences. Sometimes, for instance, the buyer in a procurement auction cannot quantify the value of non-price solution attributes or delineate between hard and soft constraints. This precludes formulating the winner determination problem (WDP) as an optimization problem. Existing decision-support aids for such situations extend an optimization framework. We present an approach that frames the decision problem as one of exploration rather than optimization. Our method relies on an algorithm that generates k-best solutions to auction WDPs. Our algorithm can incorporate hard constraints into the generation process and can scale to practical procurement auctions. We show how to extract useful guidance from k-best WDP solutions, and we evaluate our method using real bids submitted by real suppliers in an HP material parts procurement auction.

References

[1]
AHUJA, R. K., MAGNANTI, T. L., AND ORLIN, J. B. 1993. Network Flows. Prentice Hall.
[2]
BECKETT, J. 2005. The business of bidding: Reinventing auctions for better results. http: //www.hpl.hp.com/news/2005/jul-sep/auctions.html.
[3]
BOUTILIER, C., SANDHOLM, T., AND SHIELDS, R. 2004. Eliciting bid-taker non-price preferences in (combinatorial) auctions. In Proc. AAAI.
[4]
CONEN, W. AND SANDHOLM, T. 2001. Preference elicitation in combinatorial auctions {extended abstract}. In Proc. ACM E-Commerce Conf.
[5]
COUTINHO-RODRIGUES, J., CLIMACO, J., AND CURRENT, J. 1999. An interactive bi-objective shortest path approach. Computers & Operations Research 26, 789-798.
[6]
COVER, T. M. AND THOMAS, J. A. 1991. Elements of Information Theory. Wiley.
[7]
EPPSTEIN, D. 1998. Finding the k shortest paths. SIAM Journal on Computing 28, 2, 652-673.
[8]
FELLER, W. 1970. An Introduction to Probability Theory and Its Applications, Third ed. Vol. 1. John Wiley & Sons.
[9]
HOFFMAN, W. AND PAVLEY, R. 1959. A method for the solution of the nth best path problem. Journal of the ACM 6, 4 (Oct.), 506-514.
[10]
KELLERER, H., PFERSCHY, U., AND PISINGER, D. 2004. Knapsack Problems. Springer.
[11]
KELLY, T. 2004. Generalized knapsack solvers for multi-unit combinatorial auctions. In AMEC VI. http://ana.lcs.mit.edu/peyman/amec-vi-accepted.htm.
[12]
KELLY, T. AND BYDE, A. 2006. Generating k best solutions to winner determination problems. Tech. Rep. HPL-2006-40, HP Labs. Mar.
[13]
LAHAIE, S. M. AND PARKES, D. C. 2004. Applying learning algorithms to preference elicitation. In Proc. ACM E-Commerce Conf. 180-188.
[14]
PERKO, A. 1986. Implementation of algorithms for k-shortest loopless paths. Networks 16, 2, 149-160.
[15]
ROTHKOPF, M. H., PEKEC, A., AND HARSTAD, R. M. 1998. Computationally manageable combinatorial auctions. Management Science 44, 8 (Aug.), 1131-1147.
[16]
SANDHOLM, T. AND BOUTILIER, C. 2006. Combinatorial Auctions. MIT Press, Chapter 10.
[17]
VILLENEUVE, D. AND DESAULNIERS, G. 2005. The shortest path problem with forbidden paths. European Journal of Operations Research 165, 97-107.
[18]
Yukish, M. A. 2004. Algorithms to identify Pareto points in multi-dimensional data sets. Ph.D. thesis, U. Penn. Dept. of Mech. Eng.

Cited By

View all
  • (2020)Efficient meta-data structure in top-k queries of combinations and multi-item procurement auctionsTheoretical Computer Science10.1016/j.tcs.2020.01.036Online publication date: Feb-2020
  • (2016)Efficient Generation of Top-k Procurements in a Multi-item AuctionWALCOM: Algorithms and Computation10.1007/978-3-319-30139-6_15(181-193)Online publication date: 2016
  • (2010)Finding the next solution in constraint- and preference-based knowledge representation formalismsProceedings of the Twelfth International Conference on Principles of Knowledge Representation and Reasoning10.5555/3031748.3031803(425-433)Online publication date: 9-May-2010
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM SIGecom Exchanges
ACM SIGecom Exchanges  Volume 6, Issue 1
June 2006
44 pages
EISSN:1551-9031
DOI:10.1145/1150735
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2006
Published in SIGECOM Volume 6, Issue 1

Check for updates

Author Tags

  1. auctions
  2. decision support
  3. k-shortest paths
  4. knapsack problems
  5. preference elicitation
  6. procurement

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2020)Efficient meta-data structure in top-k queries of combinations and multi-item procurement auctionsTheoretical Computer Science10.1016/j.tcs.2020.01.036Online publication date: Feb-2020
  • (2016)Efficient Generation of Top-k Procurements in a Multi-item AuctionWALCOM: Algorithms and Computation10.1007/978-3-319-30139-6_15(181-193)Online publication date: 2016
  • (2010)Finding the next solution in constraint- and preference-based knowledge representation formalismsProceedings of the Twelfth International Conference on Principles of Knowledge Representation and Reasoning10.5555/3031748.3031803(425-433)Online publication date: 9-May-2010
  • (2009)Efficiently Generating k-Best Solutions to Procurement AuctionsProceedings of the 5th International Conference on Algorithmic Aspects in Information and Management10.1007/978-3-642-02158-9_8(68-84)Online publication date: 18-Jun-2009

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media