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This research aims to investigate the differences in designing the zero acceptance number single sampling plans using the apparent fraction of nonconforming and the binomial distribution against the exact convolute compound hypergeometric distribution when both types of inspection errors are present.

This research presents the derivation and uses the numerical study to compare the calculated probability of acceptance and the minimum sample size when using the present design concept of binomial distribution with true fraction of nonconforming replaced with the apparent one. Under the presences of inspection errors and zero acceptance number, the probability of acceptance is alternatively derived and presented in term of a function of the probability generating function. This research uses numerical method to determine the differences in the probability of acceptance. The computation of the minimum sample sizes are presented along with the numerical results and the comparison.

When the inspection errors are present, the probability of acceptance is extremely decreased even for 1 percent of inspection errors of Type I (rejecting good product) and Type II (accepting bad product). The binomial apparent nonconforming notions yields an over‐estimation of the probability of acceptance, comparing with the exact convolute compound hypergeometric notion under the zero acceptance single sampling plans especially at low fraction of nonconforming levels, the six sigma quality levels. The differences of the calculated probabilities of acceptance and the minimum sample sizes decrease as the inspection error of Type II increases given a fixed value of Type I error and consumer risk.

This research alternatively presents the mathematical derivation along with numerical study to assert the over‐estimation of the probability of acceptance and the minimum sample size if the existing methodology to design the zero acceptance number single sampling plans is used. This finding will help improve the sampling design strategy of the multistage production system.

This paper seeks to present an optimization‐based approach to design acceptance sampling plans by variables for controlling non‐conforming proportions in lots of items. Simple and double sampling plans with s known and unknown are addressed. Normal approximation distributions proposed by Wallis are employed to handle plans with s unknown. The approach stands on the minimization of the average sampling number (ASN) taking into account the constraints arising from the two point conditions on the operating characteristic (OC) curve. The resulting optimization problems fall under the class of mixed integer non‐linear programming (MINLP), and are solved employing GAMS. The results obtained strongly agree with classical acceptance sampling plans found in the literature, although outperforming them in some cases, and providing a general approach to address other cases.

The approach takes the form of formulation of the design of acceptance sampling plans by variables for non‐conforming proportions as optimization problems minimizing the ASN with the constraints being the acceptance probability at the controlled points of the OC curve, and subsequent solution of the mathematical programming problems arising with mathematical programming algorithms.

The results are in strong agreement with acceptance sampling plans available in the literature. The approach presented here outperforms the classical plans in some cases and its generality allows one to design other plans without the requirement of additional relations between the parameters and intensive enumerative algorithms.

The paper presents an optimization‐based approach to design robust acceptance sampling plans by variables for non‐conforming proportions that allows a general treatment and disregards the need for computational intensive enumerative‐based procedures.

Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues.

In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously.

The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate.

The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem.

The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

This purpose of this paper is to present an optimization‐based approach to support the design of attribute sampling plans for lot acceptance purposes, with the fraction of non‐conforming items being modeled by a Poisson probability distribution function.

The paper approach stands upon the minimization of the error of the probability of acceptance equalities in the controlled points of the operating curve (OC) with respect to sample size and acceptance number. It was applied to simple and double sampling plans, including several combinations of quality levels required by the producer and the consumer. Formulation of the design of acceptance sampling plans as an optimization problem, having as a goal the minimization of the squared error at the controlled points of the OC curve, and its subsequent solution employing GAMS.

The results are in strong agreement with acceptance sampling plans available in the open literature. The papers approach in some scenarios outperforms classical sampling plans and allows one to identify the lack of feasible solutions.

An optimization‐based approach to support the design of acceptance sampling plans for attributes was conceived and tested. It allows for a general treatment of these problems, including the identification of a lack of feasible solutions, as well as making possible the determination of feasible alternatives by relaxing some model constraints.

This paper aims to study the sampling methods (or design of experiments) which have a large influence on the performance of the surrogate model. To improve the adaptability of modelling, a new sequential sampling method termed as sequential Chebyshev sampling method (SCSM) is proposed in this study.

The high-order polynomials are used to construct the global surrogated model, which retains the advantages of the traditional low-order polynomial models while overcoming their disadvantage in accuracy. First, the zeros of Chebyshev polynomials with the highest allowable order will be used as sampling candidates to improve the stability and accuracy of the high-order polynomial model. In the second step, some initial sampling points will be selected from the candidates by using a coordinate alternation algorithm, which keeps the initial sampling set uniformly distributed. Third, a fast sequential sampling scheme based on the space-filling principle is developed to collect more samples from the candidates, and the order of polynomial model is also updated in this procedure. The final surrogate model will be determined as the polynomial that has the largest adjusted R-square after the sequential sampling is terminated.

The SCSM has better performance in efficiency, accuracy and stability compared with several popular sequential sampling methods, e.g. LOLA-Voronoi algorithm and global Monte Carlo method from the SED toolbox, and the Halton sequence.

The SCSM has good performance in building the high-order surrogate model, including the high stability and accuracy, which may save a large amount of cost in solving complicated engineering design or optimisation problems.

The purpose of this study was to examine the process of a custom colored carpet prototype that utilized innovative and sustainable sampling techniques.

This study adapted the prototype design process of problem definition and research; creative exploration; and implementation by LaBat and Solowski. The stages of problem definition and research and creative exploration were conducted for this study. Since only a sample of the carpet tile was developed, the implementation stage was not applicable and was replaced with an deliberation stage. The deliberation process for sustainable sampling was based on the five major performance categories of the sustainable carpet assessment standard, NSF/ASNI 140-2007: Public Health and Environment; Energy and Energy Efficiency; Bio-based Content, Recycled Content, and Environmentally Preferable Materials; Manufacturing; and Reclamation and End of Life Management.

A third party's proprietary sustainable sampling system was employed to support experimentation with multiple colorway applications, while minimizing sample waste. A custom colored commercial carpet tile that reflects one university's unique color palette was developed in collaboration with a large carpet manufacturer.

The deliberation stage was not intended to measure or award the points for the five performance categories. Moreover, the researchers are not eligible to certify carpet products. The certifiers are reputable auditors such as Scientific Certification Systems, UL Environment and NSF International. Rather, the five major performance categories of NSF/ASNI 140-2007 were employed as the framework for discussion.

Although creating a custom-colored commercial carpet tile product was a laborious and time-consuming activity, consumers in today's society have begun to expect tailored products that meet their needs. Custom colorization provides for personalization and the support of established color palettes without the anticipated higher associated expenses incurred with a completely custom product. At universities, where the home team's colors are omnipresent and extremely meaningful to stakeholders, developing custom colored carpet tile is appropriate.

Sustainable sampling was effectively utilized to facilitate a custom colored carpet tile prototype. This study fills a gap in the current literature since it incorporates a case study of the design of a sustainable interior product. Previously, few studies have tracked the inception and development of a carpet product through its design phases.

As we approach the millennium, we find ourselves in a world that places ever greater weight and significance on the outcome of polls, surveys, and market research. The advent of modern polling began with the use of scientific sampling in the mid‐1930s and has progressed vastly beyond the initial techniques and purposes of the early practitioners such as George Gallup, Elmo Roper, and Archibald Crossley. In today's environment, the computer is an integral part of most commercial survey work, as are the efforts by academic and nonprofit enterprises. It should be noted that the distinction between the use of the words “poll” and “survey” is somewhat arbitrary, with the mass media seeming to prefer “polling,” and with academia selecting “survey research.” However, searching online systems will yield differing results, hence this author's inclusion of both terms in the title of this article.