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On Ordered Weighted Reference Point Model for Multi-attribute Procurement Auctions

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Book cover Computational Collective Intelligence. Technologies and Applications (ICCCI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6922))

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Abstract

Multi-attribute auctions (also called multidimensional auctions) facilitate negotiations based on multiple attributes, thus escape from the standard price-only domain into a rich multidimensional domain that can comprise additional attributes like e.g. guarantee conditions or quality. Most multi-attribute preference models used in auction mechanisms are based on a ranking derived from weighted sum. Recently, the Reference Point Method (RPM) approach has been applied to express the multi-attribute preference models within the auction mechanisms allowing to overcome the weighted sum drawbacks. The Ordered Weighted RPM model enables us to introduce importance weights to affect achievements importance by rescaling their measures accordingly within the distribution of all achievements. The concept presented and discussed in this paper in the context of procurement auctions takes advantage of the so-called Weighted Ordered Weighted Average (WOWA) aggregations of the partial achievements.

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Authors and Affiliations

  1. Institute of Control & Computation Engineering, Warsaw University of Technology, 00-665, Warsaw, Poland

    Bartosz Kozłowski & Włodzimierz Ogryczak

Authors
  1. Bartosz Kozłowski
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  2. Włodzimierz Ogryczak
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Editors and Affiliations

  1. Gdynia Maritime University, Morska 81-87, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz

  2. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. University of Information Technology Vietnam, Km 20, Xa lo Ha Noi, Linh Trung, Thu Duc, 848, HCM City, Vietnam

    Kiem Hoang

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Kozłowski, B., Ogryczak, W. (2011). On Ordered Weighted Reference Point Model for Multi-attribute Procurement Auctions. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_29

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  • DOI: https://doi.org/10.1007/978-3-642-23935-9_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23934-2

  • Online ISBN: 978-3-642-23935-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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