A New Fuzzy Method for Assessing Six Sigma Measures

Six-Sigma has some measures which measure performance characteristics related to a process. In most of the traditional methods, exact estimation is used to assess these measures and to utilize them in practice. In this paper, to estimate some of these measures, including Defects per Million Opportunities (DPMO), Defects per Opportunity (DPO), Defects per unit (DPU) and Yield, a new algorithm based on Buckley's estimation approach is introduced. The algorithm uses a family of confidence intervals to estimate the mentioned measures. The final results of introduced algorithm for different measures are triangular shaped fuzzy numbers. Finally, since DPMO, as one of the most useful measures in Six-Sigma, should be consistent with costumer need, this paper introduces a new fuzzy method to check this consistency. The method compares estimated DPMO with fuzzy customer need. Numerical examples are given to show the performance of the method. All rights reserve

Estimating efficient value of controllable variable using an adaptive neural network algorithm: Case of a railway system

45-50This study proposes a method, using adaptive neural network (ANN), to predict, estimate and evaluate performancevariables without requiring any restrictive assumptions, taking case of a railway system. Also, by means of this method, it wouldbe possible to compare actual performance data with estimated values and route their assignable causes in future periods. Energyconsumption norm of vehicles in case of energy railway and real data of energy consumption in Iranian railway is considered

A new method to fuzzy modeling and its application in performance evaluation of tenants in incubators

As we know fuzzy modeling is one of the most powerful techniques to extract experts’ knowledge in the form of fuzzy if-then rules. In this research work, a new method to fuzzy modeling is proposed in which the main goal is to construct a fuzzy rule-base of the type of Mamdani. In the proposed method, fuzzy c-means (FCM) clustering is used for structure identification and two optimization problems are used for parameter identification. The proposed method is used to simulate experts’ knowledge for performance evaluation of tenants in incubators. The authors have implemented their proposed method in a real numerical example successfully