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In this paper an attempt is made to derive lot acceptance sampling plans based on failure censored data.  The items in the lot having multiple quality charecteristics are processed through multistage production process. We assume that quality charecteristics of the units follow exponential distribution with different parameters.  The acceptance criterion for the given lot is obtained based on the average-life of units in the sample at every stage. Two nonlinear optimiztion problems which minimize the  expected total testing costs at the acceptable quality level are solved.  Further, a sensitivity analysis study is conducted to assess the behaviour of testing costs with respect to changes in producer's risk, consumer's risk, and sample sizes. The plan parameters are obtained by solving nonlinear mixed integer programming problem by using genetic algorithm solver in MATLAB. Some numerical exapmles and two case studies are discussed to illustrate our sampling plans.

Construction of minimum cost reliability acceptance sampling plans for multistage process