A DEA-bases Approach for Multi-objective Design of Attribute Acceptance Sampling Plans

Acceptance sampling (AS), as one of the main fields of statistical quality control (SQC), involves a system of principles and methods to make decisions about accepting or rejecting a lot or sample. For attributes, the design of a single AS plan generally requires determination of sample size, and acceptance number. Numerous approaches have been developed for optimally selection of design parameters in last decades. We develop a multi-objective economic-statistical design (MOESD) of the single AS plan to reach a well-balanced compromise between cost and quality features. Moreover, a simple and efficient DEA-based algorithm for solving the model is proposed. Through a simulation study, the efficiency of proposed model is illustrated. Comparisons of optimal designs obtained using MOESD to economic model with statistical constraints reveals enhanced performance of the multi-objective model.