An Iterative Decision Rule to minimize cost of Acceptance Sampling Plan in Machine Replacement Problem

Abstract

In this paper, we presented an optimal iterative decision rule for minimizing total cost in designing a sampling plan for machine replacement problem using the approach of dynamic programming and Bayesian inferences. Cost of replacing the machine and cost of defectives produced by machine has been considered in model. Concept of control threshold policy has been applied for decision making. If the probability of producing a defective was more than a control threshold the machine is replaced otherwise its quality will be accepted and continues its production. A Numerical example along with sensitivity analysis is performed to show the application of proposed methodology.