مهدی نمازی

Uncertainty is the obvious attribute of any activity in the real world, and performance analysis of units in terms of uncertainty is one of the most important concerns of managers and planners of companies. Various approaches to data envelopment analysis have been presented so far to provide a good tool for evaluating the efficiency of units in uncertain situations, including random, fuzzy and robust approaches. In a situation where the probability distribution function of random variables is known, stochastic approach is a precise solution for problem solving. Nevertheless, previous research in this field has focused on the transformation of a random problem into a definite problem. In this research, a method for modeling data envelopment analysis is presented using computer programming. In this method, random processes are simulated, and finally, the performance distribution function is presented instead of a definite number as the unit's efficiency. In this model, a new concept is presented as the probability of efficiency, which represents the probability that the unit will be located on the efficient boundary. The proposed method is empirically applied in a real case of selecting research and development projects.

Introduction& Occupational stress is a major problem in industrial societies. Its relationship with various diseases is becoming increasingly obvious, but probably more apparent are the vast socioeconomic consequences manifested in absenteeism, labor turnover, and loss of productivity and disability pension costs. This study compared the prevalence of stress and stress among employers in different parts of the Kavir Tire factory. In this cross - sectional study, 196 workers from various parts of the factory were randomly selected by proportional sampling. Data was collected by two questionnaires: demographic and standardized job stress (Cordon). Data was analyzed by the SPSS statistical software using chi-square test and Pearson correlation (p ≤ 0. 05). 49.5 % of workers had severe job stress, 25% had job stress and 25, 5% had normal stress. Severe job stress was mainly observed in the production unit (55.8%), the administrative unit (50%) and the support unit (40.6%) Between variables (sufficient income, adequate sleep) with the level of job stress, a significant relationship was found (p=0/001). According to high stress levels among factory workers, it is necessary to provide a comprehensive program to prevent this problem.