Selection of industrial robots using the Polygons area method

Selection of robots from the several proposed alternatives is a very important and tedious task. Decision makers are not limited to one method and several methods have been proposed for solving this problem. This study presents Polygons Area Method (PAM) as a multi attribute decision making method for robot selection problem. In this method, the maximum polygons area obtained from the attributes of an alternative robot on the radar chart is introduced as a decision-making criterion. The results of this method are compared with other typical multiple attribute decision-making methods (SAW, WPM, TOPSIS, and VIKOR) by giving two examples. To find similarity in ranking given by different methods, Spearman’s rank correlation coefficients are obtained for different pairs of MADM methods. It was observed that the introduced method is in good agreement with other well-known MADM methods in the robot selection problem

A modified DDF-based super-efficiency model handling negative data

Super-efficiency model in the presence of negative data is a relatively neglected issue in the DEA field. The existing super-efficiency models have some shortcoming in practice. In this paper, the radial super-efficiency model based on Directional Distance Function (DDF) is modified to provide a complete ranking order of the DMUs (including efficient and inefficient DMUs). This model shows more reliability on differentiating efficient DMUs from inefficient ones via a new super-efficiency measure. The properties of proposed model include feasibility, monotonicity and unit invariance. Moreover, the model can produce positive outputs when data are non-negative. An empirical study in bank sector demonstrates the superiority of the proposed model