International Journal of Industrial Engineering & Production Research

In today’s competitive business situation, performance evaluation of firms is an extremely important concern of all the people who are typically stakeholders of the business game. In case of holding companies, this is a more important issue since the parent firm must permanently control the situation of its subsidiaries in their sectors to make appropriate investment decisions. This paper develops a multicriteria decision making (MCDM) approach for evaluating performance of firms considering financial and productivity criteria.. We adopt Fuzzy Analytic Hierarchy Process (FAHP) method to determine the relative importance of evaluation criteria, taking the vagueness and imprecision of human judgments into consideration. Then, we employ the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) for ranking of firms. Afterward, this paper enjoys benefit of using Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the validity of the obtained ranking results. Our approach was applied to a holding company listed in Tehran Stock Exchange (TSE) as a real case. The analysis of ranking results revealed advantages of combining these MCDM methods.

In most of the multi–criteria decision–analysis (MCDA) problems in which the Choquet integral is used as aggregation function, the coefficients of Choquet integral (capacity) are not known in advance. Actually, they could be calculated by capacity definition methods. In these methods, the preference information of decision maker (DM) is used to constitute a possible solution space. The methods which are based on optimizing an objective function most often suffer from three drawbacks. Firstly, the selection of the ultimate solution from solution set is arbitrarily done. Secondly, the solution may provide more information than whatever proposed by DM. Thirdly, DM may not fully interpret the results. Robust capacity definition methods are proposed to overcome these kinds of drawbacks, on the other hand these methods do not consider evenness (uniformity) which is a major property of capacity. Since in capacity definition methods, the preference information on only a subset of alternatives called reference alternatives, is used, defining the capacity as uniform as possible could improve its capability in evaluating non–reference alternatives. This paper proposes an algorithm to define a capacity that is based only on the preference information of DM and consequently is representative. Furthermore, it improves evenness of capacity and consequently its reliability in evaluating non–reference alternatives. The algorithm is used to evaluate power plant projects. Power plant projects are of the most important national projects in Iran and a major portion of national capital is invested on them, so these projects should be scientifically evaluated in order to figure out their performance. Case–specific criteria are considered in addition to general criteria used in project performance evaluation. The evaluation results obtained from proposed algorithm are compared with those of the most representative utility function method.