Abstract Most real-world optimization problems are subject to uncertainties in parameters. In many situations where the uncertainties can be estimated to a certain degree, various stochastic programming (SP) methodologies are used to identify robust plans. Despite substantial advances in SP, it is still a challenge to solve practical SP problems, partially due to the exponentially increasing nu...

The problems of multivariate sampling arising in the areas of stratified random sampling, two stage sampling, double sampling and response errors formulate as multiobjective convex programming problems with convex objective functions and a single linear constraint with some upper and lower bounds.

In this paper, we present a methodology for off-line handwritten character recognition. The proposed methodology relies on a new feature extraction technique based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible. Feature extraction is followed by a two...

Abstract If the sample or population has vague, inaccurate, unidentified, deficient, indecisive, fuzzy data, then available sampling plans could not be suitable to use for decision-making. In this article, an improved group-sampling plan based on time truncated life tests Weibull distribution under neutrosophic statistics (NS) been developed. We developed single and double NS. The proposed desi...

We discuss in this paper statistical inference of sample average approximations of multistage stochastic programming problems. We show that any random sampling scheme provides a valid statistical lower bound for the optimal value of the true problem. However, in order for such lower bound to be consistent one needs to employ the conditional sampling procedure. We also indicate that fixing a fea...