摘要：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.
