文章基本信息

标题：A Hybrid Distance-Based Ideal-Seeking Consensus Ranking Model
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作者：Madjid Tavana ; Frank LoPinto ; James W. Smither 等
期刊名称：Advances in Decision Sciences
印刷版ISSN：2090-3359
电子版ISSN：2090-3367
出版年度：2007
卷号：2007
DOI：10.1155/2007/20489
出版社：Hindawi Publishing Corporation
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.