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An effective total ranking model for a ranked voting system

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  • Foroughi, A.A.
  • Tamiz, M.

Abstract

In this paper an effective model to rank candidates in a preferential election is proposed. It is an extension and simplified form of a recently proposed model for ranking efficient candidates. The model consists of fewer constraints and can be used for ranking inefficient as well as efficient candidates. Some techniques are introduced to decrease the complexity of the proposed model by obtaining some of the results by inspection.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jomega:v:33:y:2005:i:6:p:491-496
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    References listed on IDEAS

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    Cited by:

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    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. Ebrahimnejad, Ali & Tavana, Madjid & Santos-Arteaga, Francisco J., 2016. "An integrated data envelopment analysis and simulation method for group consensus ranking," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 1-17.
    4. Paolo Viappiani, 2020. "Robust winner determination in positional scoring rules with uncertain weights," Theory and Decision, Springer, vol. 88(3), pages 323-367, April.
    5. Ignacio Contreras, 2010. "A Distance-Based Consensus Model with Flexible Choice of Rank-Position Weights," Group Decision and Negotiation, Springer, vol. 19(5), pages 441-456, September.
    6. Korhonen, Pekka J. & Soleimani-damaneh, Majid & Wallenius, Jyrki, 2013. "On ratio-based RTS determination: An extension," European Journal of Operational Research, Elsevier, vol. 231(1), pages 242-243.
    7. Bonifacio Llamazares, 2016. "Ranking Candidates Through Convex Sequences of Variable Weights," Group Decision and Negotiation, Springer, vol. 25(3), pages 567-584, May.
    8. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1, January.
    9. Paolo Viappiani, 2024. "Volumetric Aggregation Methods for Scoring Rules with Unknown Weights," Post-Print hal-04440153, HAL.
    10. Llamazares, Bonifacio & Pea, Teresa, 2009. "Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates," European Journal of Operational Research, Elsevier, vol. 197(2), pages 714-721, September.
    11. 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.
    12. Soltanifar, Mehdi & Shahghobadi, Saeid, 2013. "Selecting a benevolent secondary goal model in data envelopment analysis cross-efficiency evaluation by a voting model," Socio-Economic Planning Sciences, Elsevier, vol. 47(1), pages 65-74.
    13. Tavares, L. Valadares, 2012. "An acyclic outranking model to support group decision making within organizations," Omega, Elsevier, vol. 40(6), pages 782-790.
    14. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2015. "Towards a consolidation of worldwide journal rankings – A classification using random forests and aggregate rating via data envelopment analysis," Omega, Elsevier, vol. 51(C), pages 11-23.

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