Optimal Design of Acceptance Sampling Plans by Variables for Nonconforming Proportions When the Standard Deviation Is Unknown

Abstract

This article presents an optimization-based approach for the design of acceptance sampling plans by variables for controlling nonconforming proportions when the standard deviation is unknown. The variables are described by rigorous noncentral Student's t-distributions. Single and double acceptance sampling (AS) plans are addressed. The optimal design results from minimizing the average sampling number (ASN), subject to conditions holding at producer's and consumer's required quality levels. The problem is then solved employing a nonlinear programming solver. The results obtained are in close agreement with previous sampling plans found in the literature, outperforming them regarding the feasibility.

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

“…Alternatively, we can determine the above parameters of the variables chain sampling plan to minimize the ASN at AQL, which is analogous to minimizing the average sample number in the variables repetitive group sampling plans and multiple dependent state sampling plan (see Balamurali et al [27] and Balamurali and Jun [26]). Some of the authors have investigated the designing of sampling plans by using some other optimization techniques which are available in the literature (see, for example, Feldmann and Krumbholz [28], Krumbholz and Rohr [29,30], Krumbholz et al [31], and Duarte and Sariava [32,33]. The ASN for the chain sampling plan is the sample size only.…”

Optimal Design of Multiple-Objective Lot Quality Assurance Sampling (LQAS) Plans

“…The design of LQAS plans for health monitoring is similar to Acceptance Sampling plans by variables or attributes for quality control purposes. Both can be formulated as optimization problems and frequently the sought solution is the smallest sample size to control cost, see for example, Duarte and Saraiva (, ). Alternatively, one may seek to minimize the Average Sampling Number (ASN) that meets user‐specified constraints at the controlled points of the operating characteristic (OC) curve.…”