A Ranking Method Based on the Aggregate Distance Measure Function in the Value Space

Piotr Zielniewicz ()
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Piotr Zielniewicz: Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland

International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 03, 685-710

Abstract: The aim of this paper is to introduce a new MCDA method for ranking a finite set of alternatives evaluated on multiple criteria. The proposed method uses the idea of the robust ordinal regression (ROR) approach to establish the ranking scores determined by the distance measure function, however not in the criteria space, but in the value space. The preference model is composed of a set of additive value functions compatible with the preference information provided by the decision maker (DM). From among many forms of an additive preference model, we consider the model having as simple form as possible, i.e., the model that is the “closest to linear”. We define an achievement scalarizing function representing closeness to the ideal solution in the value space. A set of mix-integer linear programming (MILP) problems is then solved to determine the minimum distance scores of each alternative on the set of compatible value functions. Then, the obtained distance scores are used to rank all alternatives. A hypothetical case study is then performed to illustrate the proposed method. Finally, the result of our method is discussed and compared with results obtained by other methods based on the distance measure function (i.e., TOPSIS and VIKOR).

Keywords: Multiple criteria decision aiding; robust ordinal regression; TOPSIS method; VIKOR method (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219622017500122

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