‎in this paper, we present a two-stage model for ranking of decision making units (dmus) using interval analytic hierarchy process (ahp). since the efficiency score of unity is assigned to the efficient units, we evaluate the efficiency of each dmu by basic dea models and calculate the weights of the criteria using proposed model. in the first stage, the proposed model evaluates decision making...

We consider a problem involving estimation of a high-dimensional covariance matrix that is the sum of a diagonal matrix and a low-rank matrix, and making a decision based on the resulting estimate. Such problems arise, for example, in portfolio management, where a common approach employs principal component analysis (PCA) to estimate factors used in constructing the low-rank term of the covaria...

Decision matrix in group decision making problems depends on a lot of criteria. It is essential to know the necessity ofweight or coefficient of each criterion. Accurate and precise selection of weight will help to achieve the intended goal.The aim of this article is to introduce a linear programming model for recognizing the importance of each criterion inmulti criteria group decision making w...

This paper presents some characteristics and dilemmas of collective decision making. Collective decision making could be presented as the process of successive crystallization of dominant alternatives under the influence of different decision contexts from primary given decision potentials. This process is presented as the many-phased process of the acting of contextually dependent “energizing ...

‎In this paper, we present a two-stage model for ranking of decision making units (DMUs) using interval analytic hierarchy process (AHP). Since the efficiency score of unity is assigned to the efficient units, we evaluate the efficiency of each DMU by basic DEA models and calculate the weights of the criteria using proposed model. In the first stage, the proposed model evaluates decision making...

In recent years, substantial improvements were obtained in the effectiveness of data driven algorithms to validate the mapping of items to skills, or the Q-matrix. In the current study we use ensemble algorithms on top of existing Qmatrix refinement algorithms to improve their performance. We combine the boosting technique with a decision tree. The results show that the improvements from both t...