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Quality control plays an important role in increasing the product quality. Fuzzy control charts are more sensitive than Shewhart control chart. Hence, the correct use of fuzzy control chart leads to producing better-quality products. This area is complex because it involves a large scope of industries, and information is not well organized. In this research, we provide a literature review of the control chart under a fuzzy environment with proposing several classifications and analysis. Moreover, our research considered both attribute and variable control chart by analyzing the related researches based on the content analysis method, to classify past and current developments in the fuzzy control chart. This work has included a distribution of articles according to the journal, the case studies related to fuzzy control chart, the percentage of types of fuzzy control charts used in the literature, performance evaluation of the fuzzy control chart and summary of key points of each review paper. Finally, this paper discusses some future research direction and our overviews. The results of this study can help researchers become familiar with well-known journals, fuzzy control charts used in sample case studies, and to extract key points of each paper in minimum time.

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This study develops a inventory model to determine ordering policy for deteriorating items with shortages under markovian inflationary conditions. Markov processes include process whose future behavior cannot be accurately predicted from its past behavior (except the current or present behavior) and which involves random chance or probability. Behavior of business or economy, flow of traffic, progress of an epidemic, all are examples of Markov processes. Since the far previous inflation rate don’t have a great impact on the current inflation rate, so, It is logical to consider changes of the inflation rate as a markov process. In addition, It is assumed that the cost of the items changes as a Continuous – Time - Markov Process too. The inventory model is described by differential equations over the time horizon along with the present value method. The objective is minimization of the expected present value of costs over the time horizon. The numerical example and a sensitivity analysis are provided to analyze the effect of changes in the values of the different parameters on the optimal solution.