عنوان مقاله [English]
The ability of the process in satisfying the customers expectations is determined by using the process capability indices. In some real applications, it is possible to encounter uncertainty in the observations and specification limits of the quality characteristics. The uncertainty of quality characteristics often occurs due to the constraints in measurement systems and human subjectivity in many manufacturing industries. In these cases, the observations and specification limits are defined by fuzzy numbers. On the other hand, missing observations can be occurred as a result of insufficient sampling, high costs, and errors in measurements or during data acquisition. Moreover, machine breakdown, illegible recording of response, damaged experimental resource are common reasons for missing data. There are some methods, such as mean and regression, for estimating missing data in the literature. These methods are used to estimate data when the observations are crisp and there is no uncertainty in the observations. To the best of authors knowledge, these methods are not evaluated for estimating the missing data in the context of multivariate process capability indices with fuzzy observations. In this paper, we propose two estimation methods, including fuzzy mean and regression methods, to estimate the missing data under uncertainty. Then, the performance of the proposed estimation methods on the results of the fuzzy process capability indexCpm is evaluated when the missing data are estimated by using the proposed estimation methods. In addition, the effects of missing data percentage and correlation coefficient on the fuzzy process capability index are assessed when the missing data are estimated by using mean and regression methods. The results show that the regression method is more efficient than the mean method to estimate the missing data. The performance of the estimation methods also improves when the sample size increases. However, the performance of the proposed estimation methods deteriorates when the fuzziness increases.