Acceptance sampling plans based on EWMA yield index for the first order autoregressive process

Abstract

Abstract Acceptance-sampling plan plays an important role in quality control. Four new sampling plans based on the yield index are proposed to deal with lot sentencing for a first-order autoregressive process. The first plan is based on exponentially weighted moving average (EWMA) model. The other three plans are based on resubmitted, repetitive group sampling (RGS), and multiple dependent state repetitive (MDSR), respectively. The EWMA and MDSR models use the quality information of the current lot and previous lots. The resubmitted and repetitive group sampling plans are allowed resampling under a certain condition. We found that the sample size required for lot sentencing is the most economical for the EWMA model. Moreover, the RGS and MDSR plans are much more efficient than the traditional single sampling plan. The resubmitted scheme has the least efficiency. Considering the acceptable quality level at the producer’s risk and the lot tolerance percent defective at the consumer’s risk, a nonlinear optimisation models are proposed to determine the plan parameters. Two examples are provided to show the applicability of the proposed sampling plans.