Axiomatic foundation of the analytic hierarchy process
Management Science
Evaluating weapon system by Analytical Hierarchy Process based on fuzzy scales
Fuzzy Sets and Systems
A taxonomy of manufacturing strategies
Management Science
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Type I and type II fuzzy system modeling
Fuzzy Sets and Systems - Special issue on fuzzy modeling and dynamics
Fuzzy work-in-process inventory control of unreliable manufacturing systems
Information Sciences—Applications: An International Journal
On some new classes of implication operators and their role in approximate reasoning
Information Sciences—Informatics and Computer Science: An International Journal
A fuzzy extension of Saaty's priority theory
Fuzzy Sets and Systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy portfolio selection using fuzzy analytic hierarchy process
Information Sciences: an International Journal
Weapon selection using the AHP and TOPSIS methods under fuzzy environment
Expert Systems with Applications: An International Journal
Application of analytic hierarchy process in just-in-time manufacturing systems: a review
International Journal of Data Analysis Techniques and Strategies
Development of expert decision model to monitor precision of solar silicon wafer machine line
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
Analyzing fuzzy risk based on similarity measures between interval-valued fuzzy numbers
Expert Systems with Applications: An International Journal
A novel approach to probability distribution aggregation
Information Sciences: an International Journal
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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.