{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T04:37:24Z","timestamp":1759639044594},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGecom Exch."],"published-print":{"date-parts":[[2006,6]]},"abstract":"<jats:p>\n            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\n            <jats:italic>k<\/jats:italic>\n            -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\n            <jats:italic>k<\/jats:italic>\n            -best WDP solutions, and we evaluate our method using real bids submitted by real suppliers in an HP material parts procurement auction.\n          <\/jats:p>","DOI":"10.1145\/1150735.1150739","type":"journal-article","created":{"date-parts":[[2007,1,17]],"date-time":"2007-01-17T18:32:02Z","timestamp":1169058722000},"page":"23-34","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Generating k-best solutions to auction winner determination problems"],"prefix":"10.1145","volume":"6","author":[{"given":"Terence","family":"Kelly","sequence":"first","affiliation":[{"name":"Hewlett-Packard Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Byde","sequence":"additional","affiliation":[{"name":"Hewlett-Packard Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2006,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"AHUJA R. K. MAGNANTI T. L. AND ORLIN J. B. 1993. Network Flows. Prentice Hall.  AHUJA R. K. MAGNANTI T. L. AND ORLIN J. B. 1993. Network Flows . Prentice Hall."},{"key":"e_1_2_1_2_1","unstructured":"BECKETT J. 2005. The business of bidding: Reinventing auctions for better results. http: \/\/www.hpl.hp.com\/news\/2005\/jul-sep\/auctions.html.  BECKETT J. 2005. The business of bidding: Reinventing auctions for better results. http: \/\/www.hpl.hp.com\/news\/2005\/jul-sep\/auctions.html."},{"key":"e_1_2_1_3_1","volume-title":"Proc. AAAI.","author":"BOUTILIER C.","year":"2004"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/501158.501191"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0305-0548(98)00094-X"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"COVER T. M. AND THOMAS J. A. 1991. Elements of Information Theory. Wiley.   COVER T. M. AND THOMAS J. A. 1991. Elements of Information Theory . Wiley.","DOI":"10.1002\/0471200611"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539795290477"},{"key":"e_1_2_1_8_1","unstructured":"FELLER W. 1970. An Introduction to Probability Theory and Its Applications Third ed. Vol. 1. John Wiley & Sons.  FELLER W. 1970. An Introduction to Probability Theory and Its Applications Third ed. Vol. 1. John Wiley & Sons."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/320998.321004"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"KELLERER H. PFERSCHY U. AND PISINGER D. 2004. Knapsack Problems. Springer.  KELLERER H. PFERSCHY U. AND PISINGER D. 2004. Knapsack Problems . Springer.","DOI":"10.1007\/978-3-540-24777-7"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/11575726_6"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/988772.988800"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/net.3230160204"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.44.8.1131"},{"key":"e_1_2_1_16_1","unstructured":"SANDHOLM T. AND BOUTILIER C. 2006. Combinatorial Auctions. MIT Press Chapter 10.  SANDHOLM T. AND BOUTILIER C. 2006. Combinatorial Auctions . MIT Press Chapter 10."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2004.01.032"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Yukish M. A. 2004. Algorithms to identify Pareto points in multi-dimensional data sets. Ph.D. thesis U. Penn. Dept. of Mech. Eng.   Yukish M. A. 2004. Algorithms to identify Pareto points in multi-dimensional data sets. Ph.D. thesis U. Penn. Dept. of Mech. Eng.","DOI":"10.2514\/6.2004-4324"}],"container-title":["ACM SIGecom Exchanges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1150735.1150739","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T19:33:46Z","timestamp":1672256026000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1150735.1150739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,6]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2006,6]]}},"alternative-id":["10.1145\/1150735.1150739"],"URL":"https:\/\/doi.org\/10.1145\/1150735.1150739","relation":{},"ISSN":["1551-9031"],"issn-type":[{"value":"1551-9031","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,6]]},"assertion":[{"value":"2006-06-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}