Quality and Reliability Engineering International

Cover image for Vol. 33 Issue 5

Edited By: Aarnout C. Brombacher and Douglas C. Montgomery

Impact Factor: 1.366

ISI Journal Citation Reports © Ranking: 2016: 28/44 (Engineering Industrial); 34/85 (Engineering Multidisciplinary); 44/83 (Operations Research & Management Science)

Online ISSN: 1099-1638


Call for papers – Nonparametric Statistical Process Control Charts

Statistical process control (SPC) charts are widely used in the industry for monitoring the stability and the efficacy of processes (e.g. manufacturing processes, health care systems, internet traffic flow, and so forth) based on observed time-ordered data. Traditional control charts require the assumption that the process response distribution follows a parametric form (e.g. normal). In practice, however, this assumption may not hold, in other words, the process may not follow the pre- specified parametric distribution. In the literature, it has been well demonstrated that results from the traditional control charts using the pre-specified distribution in their design may not be reliable because their actual false alarm rates could be substantially larger or smaller than the assumed false alarm rate. A direct consequence of this could be that much labor and many resources are wasted, or that many defective products are manufactured without notice. Therefore, in cases when no parametric form of the process response distribution is available or when no parametric form is validated properly beforehand, control charts without requiring the specication of a parametric form for the process response distribution, or simply nonparametric (distribution-free) statistical process control (NSPC) charts, should be considered.

There has been a huge growth in NSPC research in recent years. The goal of this special issue is to review and highlight what has been done, bring to light the latest cutting edge research in the area and provide future directions. The special issue will cover all topics related to NSPC, including but not limited to (i) phase I NSPC, (ii) phase II NSPC, (iii) NSPC for monitoring discrete or categorical data, and (iv) new and innovative applications including case studies involving NSPC. Papers must contain high-quality original contributions, and be prepared in accordance with the QREI standards and guidelines. Submitted papers should be original, not previously published, and not under consideration for publication elsewhere. All papers will be reviewed following the regular review procedure of QREI.

Paper Submission
Submit manuscripts through the online Manuscript Central system at http://mc.manuscriptcentral.com/qre. Please select Special Issue - Nonparametric Statistical Process Control under Manuscript Type of your submission.

Guest Editorial Board

Subha Chakraborti
Professor of Statistics
University of Alabama
Tuscaloosa, AL 35487

Peihua Qiu
Professor of Statistics
University of Minnesota
Minneapolis, MN 55455

Amitava Mukherjee
Faculty in Quantitative Methods
Indian Institute of Management Udaipur
Rajasthan, 313001

Important Dates 1 February 2014: Last date for paper submission for full consideration.

Call for papers – Data Mining

We are pleased to announce a call for papers for a Data Mining Special Issue of Quality and Reliability Engineering International.

Data mining has attracted huge amounts of interest in recent years. This Special Issue of QREI welcomes submissions concerning data mining methodologies and applications from a broad scope of research areas such as engineering, health care, bioinformatics, social sciences, finance etc.

Papers must be prepared in accordance with journal standards and guidelines, which can be accessed here. Submitted papers should be original, not previously published, and not under consideration for publication elsewhere. All papers will be reviewed following the journal’s regular procedure.

Submission should me made though the journal’s online system here. Please select the correct Special Issue from the dropdown list under Manuscript Type (Data Mining – Editors: Jing Li and Murat Kulahci).

Guest Editors:
Jing Li, Assistant Professor, Arizona State University, USA
Murat Kulahci, Associate Professor, Technical University of Denmark

Important dates:
1st September 2013: Paper submission deadline
1st February 2014: Completion of review process
1st May 2014: Final manuscripts due
1st August 2014: Tentative publication date