Novel methodologies and a comparative study for manufacturing systems performance evaluations

The purpose of this work is to establish complex fuzzy methodologies in the evaluation of a manufacturing system’s performance. Many empirical studies have been presented about the evaluation of manufacturing system’s performance. However, the performance evaluation is quite subjective, since it relies on the individual judgment of the managers who have different, various and multi-factor assessment methods of a system’s performance. In this study, two fuzzy modeling designs were developed and in the construction of the models, a hierarchy process was used. In the first method, the performance factors and the Analytic Hierarchy Process (AHP) were fuzzified and the use of fuzzy numbers and a fuzzy AHP for this problem was recommended. Also, the relative importance of these factors with respect to each other and their contribution to the overall performance was quantified with fuzzy linguistic terms. In the other method, we proposed Approximate Reasoning (AR) based on experts’ knowledge which is represented with the collection of the rules. These fuzzy rule bases are “if-then” linguistic rules that are formed with linguistic variables such as poor, below average, average, above average and superior. Additionally, the problem was structured with the normal AHP and System-With-Feedback (SWF), Finally, these methods were compared. The results showed that fuzzy AHP leads to the best result. It is expected that the recommended models would have an advantage in the competitive manufacturing including cost, flexibility, quality, speed and dependability.