Multi-Criteria Decision Making Based on Correlation Coefficient of Triangular Intuitionistic Fuzzy Numbers

We study a multi-criteria fuzzy decision-making method based on weighted triangular intuitionistic fuzzy number correlation coefficients. Under the scenario that criteria weights for alternatives are completely unknown, triangular intuitionistic fuzzy method can not only supplement the insufficiency of the method based on the distance but also endow more information to the estimation and reduce the loss of evaluation information.Among the triangular numbers, two boundary numbers are the maximum and minimum values of the interval respectively, and the medium number is the most possible value under subjective estimation. Using this method,we propose a new way to obtain the criteria weights with more information quantity. By ranking the relative closeness of the weighted correlation coefficients between each alternative, and the critical and ideal alternatives,we show the method to figure out the most suitable alternative based on the expected criteria. An illustrative example is also taken into account to prove the effectiveness of the model.     

Optimal inspection and replacement strategy for systems subject to two types of failures with adjustable inspection intervals

The inspection activities are often carried out to detect possible indication of failures in plant systems. This paper considers a single unit system subject to two types of failures, where one failure mode is the traditional 0-1 logic failure and the other failure mode is described by a two-stage failure process. Adjustable inspections are used to detect the defective stage of the latter. We assume that the inspection duration gets shorter and shorter with a constant ratio. At the same time, preventive replacement is used to avoid the possible failure due to the former failure mode. The renewal process of this system is analyzed and the expected long-run cost per unit time (ELRCUT) is derived. The optimal inspection period and the preventive replacement interval to minimize ELRCUT are studied. At last, a numerical example is presented to illustrate the proposed model.     

Software Reliability Growth Model Considering First-Step and Second-Step Fault Dependency

As one of the most important indexes to evaluate the quality of software, software reliability experiences an increasing development in recent years. We investigate a software reliability growth model(SRGM). The application of this model is to predict the occurrence of the software faults based on the non-homogeneous Poisson process(NHPP). Unlike the independent assumptions in other models, we consider fault dependency. The testing faults are divided into three classes in this model: leading faults, first-step dependent faults and second-step dependent faults. The leading faults occurring independently follow an NHPP, while the first-step dependent faults only become detectable after the related leading faults are detected. The second-step dependent faults can only be detected after the related first-step dependent faults are detected. Then, the combined model is built on the basis of the three sub-processes. Finally, an illustration based on real dataset is presented to verify the proposed model.