The truncated life test is usually applied for reducing experiment time. The lifetime of a product is assumed as its quality characteristic, and the sequential sampling (SS) plan is applied in the context of the truncated life test. In SS, the samples are selected from the lot stage by stage. In each stage, the total number of inspected items and the total number of defective items are specified, and then it is decided whether to continue sampling or to make a decision about the lot. A procedure is provided for computing the operation characteristic curve and average sample number (ASN) in the proposed SS plan. Moreover, a repetitive group sampling plan and a double sampling (DS) plan are also designed based on the truncated life test. Performance of the SS plan is compared with the DS plan and repetitive sampling plan. The application of these three sampling plans are illustrated in the industry using a real example. Finally, results of the comparison study indicate that the proposed SS plan has a better performance and could significantly reduce the ASN.

In this article, we present an acceptance sampling plan for machine replacement problem based on the backward dynamic programming model. Discount dynamic programming is used to solve a two-state machine replacement problem. We plan to design a model for maintenance by considering the quality of the item produced. The purpose of the proposed model is to determine the optimal threshold policy for maintenance in a finite time horizon. We create a decision tree based on a sequential sampling including renew, repair and do nothing and wish to achieve an optimal threshold for making decisions including renew, repair and continue the production in order to minimize the expected cost. Results show that the optimal policy is sensitive to the data, for the probability of defective machines and parameters defined in the model. This can be clearly demonstrated by a sensitivity analysis technique.

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