Trust-based performance measurement using fuzzy operators

Performance assessment is a critical aspect for any organization, as it provides them with the means to measure their performance. Decision makers and top management need to gain a comprehensive view of the capabilities and performance of decision making units (DMU's) in order to make efficient decisions and beneficial improvements. In this study a novel model has been proposed to place performance assessment outputs in linguistic form, which utilize proper trust labels. Trust labels provide explicit qualitative scales, instead of representing pure numerical data, which are more meaningful for top manager. Fifteen scenarios are formed based on two main factors: the number of decision making units and the number of timeslots, which together form the basis of the proposed method for performance assessment. The efficiency rates of the current, previous and following years, along with the average efficiency and standard deviation, are the five inputs to this model. The approach uses time series forecasting to predict the future efficiency rate and is armed with an Auto Correlation Function (ACF) for input selection. The model utilizes fuzzy t-norm and s-norm as the final modeling tools. To show the applicability and superiority of the proposed model, it is applied to a data set provided by running a simulation structured by a unique logic.