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Three new models for preference voting and aggregation

Author

Listed:
  • Y M Wang

    (Fuzhou University
    The University of Manchester)

  • K S Chin

    (City University of Hong Kong)

  • J B Yang

    (The University of Manchester)

Abstract

Preference voting and aggregation require the determination of the weights associated with different ranking places. This paper proposes three new models to assess the weights. Two of them are linear programming (LP) models which determine a common set of weights for all the candidates considered and the other is a nonlinear programming (NLP) model that determines the most favourable weights for each candidate. The proposed models are examined with two numerical examples and it is shown that the proposed models cannot only choose a winner, but also give a full ranking of all the candidates.

Suggested Citation

  • Y M Wang & K S Chin & J B Yang, 2007. "Three new models for preference voting and aggregation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1389-1393, October.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:10:d:10.1057_palgrave.jors.2602295
    DOI: 10.1057/palgrave.jors.2602295
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    References listed on IDEAS

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    1. Obata, Tsuneshi & Ishii, Hiroaki, 2003. "A method for discriminating efficient candidates with ranked voting data," European Journal of Operational Research, Elsevier, vol. 151(1), pages 233-237, November.
    2. Green, Rodney H. & Doyle, John R. & Cook, Wade D., 1996. "Preference voting and project ranking using DEA and cross-evaluation," European Journal of Operational Research, Elsevier, vol. 90(3), pages 461-472, May.
    3. Wade D. Cook & Moshe Kress, 1990. "A Data Envelopment Model for Aggregating Preference Rankings," Management Science, INFORMS, vol. 36(11), pages 1302-1310, November.
    4. Foroughi, A.A. & Tamiz, M., 2005. "An effective total ranking model for a ranked voting system," Omega, Elsevier, vol. 33(6), pages 491-496, December.
    5. Hashimoto, Akihiro, 1997. "A ranked voting system using a DEA/AR exclusion model: A note," European Journal of Operational Research, Elsevier, vol. 97(3), pages 600-604, March.
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    Cited by:

    1. Pishchulov, Grigory & Trautrims, Alexander & Chesney, Thomas & Gold, Stefan & Schwab, Leila, 2019. "The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 211(C), pages 166-179.
    2. Llamazares, Bonifacio & Peña, Teresa, 2013. "Aggregating preferences rankings with variable weights," European Journal of Operational Research, Elsevier, vol. 230(2), pages 348-355.
    3. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2016. "Revisiting the standing of international business journals in the competitive landscape," Journal of World Business, Elsevier, vol. 51(4), pages 487-498.
    4. Byeong Seok Ahn, 2017. "Aggregation of ranked votes considering different relative gaps between rank positions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1307-1311, November.
    5. Yanjin He & Hosang Jung, 2018. "A Voting TOPSIS Approach for Determining the Priorities of Areas Damaged in Disasters," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    6. Seyed Saeed Hosseinian & Hamidreza Navidi & Abas Hajfathaliha, 2012. "A New Linear Programming Method for Weights Generation and Group Decision Making in the Analytic Hierarchy Process," Group Decision and Negotiation, Springer, vol. 21(3), pages 233-254, May.
    7. Xuefeng Zhang, 2019. "User selection for collaboration in product development based on QFD and DEA approach," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2231-2243, June.

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