Multiple dependent state repetitive group sampling plan for Burr XII distribution

ABSTRACT

In material engineering application, the failure time of material due to weakness in material (fatigue) is usually caused by repeated variations of stress. The failure time is modeled by statistical distributions. In this article, an attribute multiple state repetitive group sampling plan is developed assuming that the life time follows the Burr Type XII distribution. The plan parameters are determined by considering two points on operating characteristic (OC) curve. Tables are given for the practical use. The advantages of the proposed plan are discussed over the single sampling plans. Examples are given to illustrate the proposed plan.

KEYWORDS:

• Burr type XII distribution
• consumer's risk
• multiple state repetitive group sampling plans
• operating characteristics values
• producer's risk
• truncated life test

Funding

The author, Muhammad Aslam, acknowledges with thanks DSR for technical and financial support.

About the authors

Muhammad Aslam did his M.Sc in Statistics (2004) from GC University Lahore, M. Phil in Statistics (2006) from GC University Lahore, and Ph.D. in Statistics (2010) from National College of Business Administration & Economics Lahore. He worked as a lecturer of Statistics in Edge College System International from 2003-2006. He also worked as Research Assistant in the Department of Statistics, GC University Lahore from 2006 to 2008. Then he joined the Forman Christian College University as a lecturer in August 2009. He worked as Assistant Professor in the same University from June 2010 to April 2012. He worked in the same department as Associate Professor from June 2012 to October 2014. Currently, he is working as an Associate Professor of Statistics in department of Statistics, King Abdul-Aziz University Jeddah, Saudi Arabia. He has published more than 145 research papers in national and international journals. He is the author of one book published in Germany. He is also HEC approved PhD supervisor since 2011. He supervised 2 Ph.D. thesis, more than 20 M.Phil theses and 3 M.Sc theses. He received meritorious services award in research from National College of Business Administration & Economics Lahore in 2011. He received Research Productivity Award for the year 2012 by Pakistan Council for Science and Technology. He is the member of editorial board of Electronic Journal of Applied Statistical Analysis and Pakistan Journal of Commence and Social sciences. His areas of interest include reliability, decision trees, Industrial Statistics, acceptance sampling, rank set sampling and applied Statistics.

Muhammad Azam received the Ph.D. degree in Statistics from the University of Innsbruck, Austria in 2010. He joined the Department of Statistics at the Forman Christian College University Lahore in 2010, where he now holds the position of Associate Professor. His research interests include topics related to survey sampling, quality control, decision trees, and ensemble classifiers.

Chi-Hyuck Jun is a professor in Department of Industrial and Management Engineering, POSTECH. He received a B.S. in mineral and petroleum engineering from Seoul National University, an M.S. in Industrial Engineering KAIST, and a Ph.D. in Operations Research from University of California, Berkeley. Since 1987 he has been with the Department of Industrial and Management Engineering, POSTECH. He is interested in data mining and reliability/quality.

Acknowledgments

The authors are deeply thankful to the editor and reviewers for their useful suggestions to improve the manuscript. This article was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah.