<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adel Ahmadi Nadi</style></author><author><style face="normal" font="default" size="100%">Bahram Sadeghpour Gildeh</style></author><author><style face="normal" font="default" size="100%">Robab Afshari</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal design of overall-yield-based variable repetitive sampling plans for process with multiple characteristics</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Mathematical Modelling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0307904X1930719X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">81</style></volume><pages><style face="normal" font="default" size="100%">194-210</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this study, we developed two repetitive types of sampling plans for processes with multiple quality characteristics based on the overall yield index&amp;nbsp;S&lt;sup&gt;T&lt;/sup&gt;&lt;sub&gt;pk&lt;/sub&gt;. These plans can be implemented for mutually independent and normally distributed characteristics. The plans are optimally designed based on the asymptotic sampling distribution of&amp;nbsp;S&lt;sup&gt;T&lt;/sup&gt;&lt;sub&gt;pk&lt;/sub&gt;&amp;nbsp;using an efficient&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/nonlinear-optimization&quot; title=&quot;Learn more about nonlinear optimization from ScienceDirect's AI-generated Topic Pages&quot;&gt;nonlinear optimization&lt;/a&gt;&amp;nbsp;algorithm. During the solution of optimization problems, the average sample number required for inspection and the contract requirements are treated as an objective function and constraints, respectively. The optimal parameters were determined for use in industrial environments with various combinations of requirements in tables. A simulation study was also conducted to show that the tabulated parameters based on the results obtained by large sample theory can guarantee the specified risks for finite sample sizes. Moreover, the limitations of the proposed plans with respect to the sample size were analyzed based on extensive simulations. Numerical calculations and&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/mathematics/graphical-illustration&quot; title=&quot;Learn more about graphical illustrations from ScienceDirect's AI-generated Topic Pages&quot;&gt;graphical illustrations&lt;/a&gt;&amp;nbsp;are presented to demonstrate the&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/mathematics/sigma-property&quot; title=&quot;Learn more about properties from ScienceDirect's AI-generated Topic Pages&quot;&gt;properties&lt;/a&gt;&amp;nbsp;of the proposed plans. In addition, the advantages of the schemes are discussed compared with existing plans. Finally, the efficient plan is applied to two real industrial problems.</style></abstract></record></records></xml>