Multi Criteria Decision Making (MCDM) Models in Fuzzy and Non Fuzzy Environments

In decision making science, an important aspect is to select one strategy from available ones and to prioritize. Multi criteria decision making methods, especially fuzzy MCDM have made their way in to this field for several years. Among them, analytical hierarchy process (AHP) method and technique for order preference by similarity to ideal solution (TOPSIS) have been employed more than other technique and methods have. Function productivity is among many different factors. In the climate of decision making to increase productivity. Therefore, raised question is that on which factor and how much we should put emphasis. This study tries to answer this question using MCDM models. For this reason, after primary data was collected with identification questionnaire and effective factors were categorized by using statistical analysis done with SPSS software, a primary refinement was carried out on factors and criteria. Next these factors are ranked by analytical hierarchy process (AHP), fuzzy technique for order preference by similarity to ideal solution (FUZZY TOPSIS), and Fuzzy AHP methods. Which are among the most important multi criteria techniques?Given that the results from above methods.In some cases, are not in agreement with each other, combined POSET technique was used reach consensus on ranking criteria. Finally disagreement between the results was examined by using freedman’s statistical test and spearman’s correlation coefficient. With regard to the results of this study, a combined ranking method, taking ranking means, was employed to make decisions on prioritizing productivity objectives of west region power corporation. Since it was impossible to choose optimal rankingmethod from fuzzy and non fuzzy methods. Eventually, important criteria in making policy on human force productivity were identified from management factors. Human force and customers, with management factors being the most important ones separated by management information system index