Result: RECTIFYING INSPECTION FOR DOUBLE SAMPLING PLANS WITH FUZZY LOGIC UNDER ZERO-INFLATED POISSON DISTRIBUTION USING IN PYTHON.

Acceptance sampling is a statistical quality control technique used in manufacturing to determine whether to accept or reject a batch of products based on the number of defects obtain in a sample. Among the various sampling plans, the double sampling plan more effective because it often delivers more reliable results in selecting quality lots than other plans. In most of the real life situation, it is not easy found the product as strictly defective or non-defective. In some situation, quality of the product can be classified several types which are expressed as good, almost good, bad, not so bad and so on. This is causes fuzzy logic comes into play. Fuzzy set theory is most powerful mathematical tool, it can deal incomplete and imprecise information. In this paper Double Sampling Plans (DSPs) are derived when non conformities are say imprecise and these imprecisions are model through ZIP distribution. It analyzes, the effectiveness of these sampling plans by comparing vital metrics such as Average Outgoing Quality (AOQ) and Average Total Inspection (ATI) using both fuzzy and crisp environments. These findings are appraised as both numerically and graphically, showing that whether the process quality is either extremely good or very bad, the AOQ curve will be lower, the plan's able to effectively control product quality. [ABSTRACT FROM AUTHOR]