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Abstract: One of the primary concerns on target setting the electricity distribution companies is the uncertainty on input/output data. In this paper, an interactive robust data envelopment analysis (IRDEA) model is proposed to determine the input and output target values of electricity distribution companies with considering the existence perturbation in data. Target setting is implemented with the uncertain data and the decision maker (DM) can search the envelop frontier and find the targets based on his preference. In order to search the envelop frontier, the paper combine the DEA and multi-objective linear programming method such as STEM. The proposed method of this paper is capable of handling uncertainty in data and finding the target values according to the DM’s preferences. To illustrate ability the proposed model, a numerical example is solved. Also, the input and output target values for some of the electricity distribution companies in Iran are reported. The results indicate that the IRDEA model is suitable for target setting based on DM’s preferences and with considering uncertain data. [Copyright &y& Elsevier]

Abstract: Conventional super-efficiency data envelopment analysis (DEA) models require the exact information of inputs or outputs. However, in many real world applications this simple assumption does not hold. Stochastic super-efficiency is one of recent methods which could handle uncertainty in data. Stochastic super-efficiency DEA models are normally formulated based on chance constraint programming. The method is used to estimate the efficiency of various decision making units (DMUs). In stochastic chance constraint super-efficiency DEA, the distinction of probability distribution function for input/output data is difficult and also, in several cases, there is not enough data for estimating of distribution function. We present a new method which incorporates the robust counterpart of super-efficiency DEA. The perturbation and uncertainty in data is assumed as ellipsoidal set and the robust super-efficiency DEA model is extended. The implementation of the proposed method of this paper is applied for ranking different gas companies in Iran. [Copyright &y& Elsevier]

Abstract: The key issue for decision making in stock trading is selection of the right stock at the right time. In order to select the superior stocks (alternatives) for investment, a finite number of alternatives have to be ranked considering several and sometimes conflicting criteria that often are vague and have uncertainty conditions. Therefore, we are faced with a special Multiple Criteria Decision Making (MCDM) problem. The purpose of this paper is to develop a fuzzy expert system for selecting superior stocks in order to encounter the uncertainty of stock portfolio recommendation and model the recommendation rules which experts at Tehran Stock Exchange (TSE) use for portfolio recommendation. The results of implementing the designed fuzzy expert system at TSE were affirmative. [Copyright &y& Elsevier]