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Journal of Applied Mathematics and Decision Sciences
Volume 2007 (2007), Article ID 20489, 18 pages
Research Article

A Hybrid Distance-Based Ideal-Seeking Consensus Ranking Model

1Management Department, School of Business, La Salle University, 1900 West Olney Avenue, Philadelphia, PA 19141, USA
2Management Department, School of Business La Salle University, 1900 West Olney Avenue, Philadelphia, PA 19141, USA

Received 1 December 2006; Revised 11 March 2007; Accepted 23 April 2007

Academic Editor: Mahyar A. Amouzegar

Copyright © 2007 Madjid Tavana et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ordinal consensus ranking problems have received much attention in the management science literature. A problem arises in situations where a group of k decision makers (DMs) is asked to rank order n alternatives. The question is how to combine the DM rankings into one consensus ranking. Several different approaches have been suggested to aggregate DM responses into a compromise or consensus ranking; however, the similarity of consensus rankings generated by the different algorithms is largely unknown. In this paper, we propose a new hybrid distance-based ideal-seeking consensus ranking model (DCM). The proposed hybrid model combines parts of the two commonly used consensus ranking techniques of Beck and Lin (1983) and Cook and Kress (1985) into an intuitive and computationally simple model. We illustrate our method and then run a Monte Carlo simulation across a range of k and n to compare the similarity of the consensus rankings generated by our method with the best-known method of Borda and Kendall (Kendall 1962) and the two methods proposed by Beck and Lin (1983) and Cook and Kress (1985). DCM and Beck and Lin's method yielded the most similar consensus rankings, whereas the Cook-Kress method and the Borda-Kendall method yielded the least similar consensus rankings.