An Iterative Decision Rule to Minimize Cost of Acceptance Sampling Plan inMachine Replacement Problem

In this paper, an optimal iterative decision rule for minimizing total cost in designing a sampling planfor machine replacement problem is presented using the approach of dynamic programming andBayesian inferences. Cost of replacing the machine and cost of produced defectives have beenconsidered in model. Concept of control threshold policy has been applied for decision making. If theprobability of producing a defective was more than a control threshold then the machine is replacedotherwise its performenace will be accepted and continues its production. A numerical example alongwith sensitivity analysis is performed to show the application of proposed methodology