In zero acceptance number SAMPLING plans, the sample items of an incoming lot are inspected one by one. The proposed method in this research follows these rules: if the number of nonconforming items in the first sample is equal to zero, the lot is accepted but if the number of nonconforming items is equal to one, then second sample is taken and the policy of zero acceptance number would be applied for the second sample. In this paper, a mathematical model is developed to DESIGN single stage and double stage SAMPLING plans. Proposed model can be used to determine the optimal tolerance limits and sample size. In addition, a sensitivity analysis is done to illustrate the effect of some important parameters on the objective function. The results show that the proposed two stage SAMPLING plan has better performance than single stage SAMPLING plan in terms of total loss function, sample size and robustness.

A novel approach to determine optimal SAMPLING locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimization problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of SAMPLING devices as a surrogate of SAMPLING DESIGN cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal SAMPLING locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic SAMPLING DESIGN optimisation problem is solved for a number of randomly generated calibration model parameter samples. The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.

We introduce a modified Poisson SAMPLING, with a fixed lower bound of sample size. The DESIGN is a combination of simple random SAMPLING and Poisson SAMPLING. Simple random SAMPLING is used to compensate for the lack of sample size from remaining elements in the finite population, after execution of a Poisson SAMPLING. At the first stage, the units are sampled independently with given inclusion probabilities. But in the second stage, inclusion probabilities are dependent to each other. Because it is important to know, which of the elements are selected in the first stage and which of them are remained. Some advantages of our DESIGN are: simple performance, controlling sample size, ability to perform the method of probability proportional to size. The simulations show that the DESIGN can dominate its rival DESIGN in probability proportional to size SAMPLING.

Dear Editor, We read the valuable manuscript with the title: Association of modified Nordic diet with cardiovascular risk factors among type 2 diabetes patients: a cross-sectional study that published in J Cardiovasc Thorac Res. 1 In this manuscript authors have said “ cluster random SAMPLING methods were used to select participants. Throughout the five sectors of Isfahan, we randomly chose two centers (clinics) from each area. Based on previously calculated mean and standard deviations for BMI in this population, our target number of participants was 143”

In acceptance SAMPLING plans, the decisions on either accepting or rejecting a specific batch is still a challenging problem. In order to provide a desired level of protection for customers as well as manufacturers, in this paper, a new acceptance SAMPLING DESIGN is proposed to accept or reject a batch based on Bayesian modeling to update the distribution function of the percentage of nonconforming items. Moreover, to determine the required sample size the backwards induction methodology of the decision tree approach is utilized. A sensitivity analysis that is carried out on the parameters of the proposed methodology shows the optimal solution is affected by initial values of the parameters. Furthermore, an optimal (n, c) DESIGN is determined when there is a limited time and budget available and hence the maximum sample size is specified in advance.