minimum angle method

An essential tool for examining the quality of manufactured products is acceptance sampling. This research applies the concept of minimum angle method to extend two variables sampling plans including the variables multiple dependent state (VMDS) sampling plan and the variables repetitive group sampling (VRGS) plan on the basis of the process yield index Spk. Optimal parameters of acceptance sampling plans can be determined by solving a non-linear optimization model with the following conditions: 1) The objective function of the plan is to minimize the average sample number. 2) Constraints are set in a way that the compliance rate will be satisfied with the ideal operating characteristic (OC) curve as well as the producer’s and costumer’s risks. The assessment of the proposed plans reveals that by increasing the rate of convergence to the ideal OC curve, the proposed VRGS plan performs better than the proposed VMDS plan in terms of the average sample number. A numerical example is considered to reveal the applicability of the proposed acceptance sampling plans.

The purpose of this article is to present an optimization model for designing an acceptance sampling plan based on cumulative sum of run length of conforming items. The objective is to minimize the total loss including both the producer and consumer losses. The concept of minimum angle method is applied to consider producer and consumer risks in the optimization model. Also the average number of inspection is considered in the constraint of the model.A practical case study is solved and a sensitivity analysis is performed for elaborating the effect of some important parameters on the objective function. The results of sensitivity analysis showed that the proposed model performance is logical, reliable in all the cases and also has better performance in comparison with classical method in most of the cases. A computational experiment is done to compare the different sampling schemes. The results of computational experiment showed that the proposed model has better performance due to smaller ANI value in all cases.