Published since April 1, 2001

Stochastics and Quality Control

ISSN: 2367-2404

Editor-in-chief: George P. Yanev

Founded by: Elart von Collani

Editorial Board: Justin Chimka, Boyan Dimitrov, Miguel González, Olgierd Hryniewicz, Vladimir K. Kaishev, Magdi S. Moustafa, S.P. Mukherjee, Ken Nishina, Abdur Rahim, Erwin M. Saniga

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About this journal

Objective
Stochastics and Quality Control is devoted to all fields related to reliability and quality control in economy, finance, manufacturing, production and inventory. It primarily publishes research articles, but survey articles on a topical area inside the scope are considered, too.

The journal pays special attention to the interdisciplinary character of the topic. Not only mathematicians, but also scientists, economists as well as scholars from other disciplines contribute papers. The methods and results published thus are of interest to various applications in real life.

Topics

• Applied Stochastic Processes
• Branching Processes: Theory and Applications
• Reliability
• Ordered Data Analysis
• Statistical Methodology
• Business Management
• Economy and Finance

Article formats
Research and survey articles

Information on submission process

Your Benefits

Your benefits

Up-to-date research on economic quality control
Interdisciplinary approach to the topic
Worldwide-known editors and reviewers
Rapid online publication of contributions “ahead-of-print”

History

History
Economic Quality Control (now, Stochastics and Quality Control) has emerged from the identically named Newsletter of the “Würzburg Research Group on Quality Control” which was founded in 1986 by Bernhard F. Arnolds, Elart von Collani and John Sheil. Until 2009 the journal was published in cooperation with Heldermann Verlag.　

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Volume 38 Issue 2

December 2023

Publicly Available December 1, 2023

Frontmatter

Page range: i-iv

Download PDF

July 25, 2023

A Higher-Order Markov Model for a Hybrid Inventory System with Probabilistic Remanufacturing Demand

Ali Khaleel Dhaiban

Page range: 47-62

Abstract

This study develops a higher-order Markov model (HOM) for an inventory system with remanufacturing, substitution, and lost sales. Defective and disposed items are other factors that are considered in addition to probabilistic demand for both manufacturing and remanufacturing items. One year is the warranty period for items manufactured, and items sold return from customers to the manufacturer in increasing cumulative percentages over the months of the year. To the best our knowledge, a higher-order Markov model has rarely been used in a hybrid inventory system. The challenge is how to determine the steady state of the system with the probable demand for manufacturing and remanufacturing. We propose a new search algorithm to select the best control strategy from several strategies, and then compare it with the two-phase local search algorithm. Each state deals with (12) a probabilistic demand (policy), so the system steady state is set to (22632) policies in total for each production plan. The results showed profit maximization using the new search algorithm compared with the two-phase local search algorithm. Also, an increase in defective and returned items over time, and therefore an increase in remanufactured items. But it does not satisfy all the demand, so manufacturing increases over time due to substitution. Substitution strategy leads to increase the expected average profit.

November 2, 2023

Quality Control Using Convolutional Neural Networks Applied to Samples of Very Small Size

Rallou A. Chatzimichail, Aristides T. Hatjimihail

Page range: 63-78

Abstract

Artificial neural networks (NNs) have been extensively studied for their application to quality control (QC) to monitor the conformity of processes to quality specifications. However, the requirement of at least five QC measurements increases the associated costs. This study explores the potential of using NNs on samples of QC measurements of very small size. To achieve this, three one-dimensional (1-D) convolutional NNs (CNNs) were designed, trained, and tested on datasets of n -tuples of simulated, standardized, normally distributed QC measurements, where 2 ≤ n ≤ 4 {2\leq n\leq 4} . The performance of the designed CNNs was compared to that of statistical QC functions applied to samples of equal sizes, maintaining equal probabilities for false rejection. The results demonstrated that for n -tuples of QC measurements distributed as 𝒩 ⁢ ( 0 , s 2 ) \mathscr{N}(0,s^{2}) , where 1.2 < s ≤ 9.0 1.2<s\leq 9.0 , the designed CNNs outperformed their statistical QC functions counterparts. Therefore, the use of 1-D CNNs applied to samples of two to four QC measurements can effectively enhance the detection of nonconformity of a process to quality specifications. This approach has the potential to significantly reduce the costs of QC measurements and improve the overall efficiency of the QC process.

July 25, 2023

Design and Optimization of c-Control Chart Using a Triple Sampling Scheme

José Jorge Muñoz, Manuel J. Campuzano, Verónica Deibe-Blanco

Page range: 79-93

Abstract

In this paper, a c-control chart using a triple sampling scheme (TS-c) is studied. The chart design, procedure, and a bi-objective optimization model are given to optimize the TS-c-chart. The Average Run Length for in-control and out-of-control ( ARL 1 \mathrm{ARL}_{1} ), and Average Sample Number metrics are calculated. A Comparison among TS-c, Fixed parameters c (FP-c), VSS-c, EWMA-c, and Double Sampling c (DS-c) control charts are carried out in terms of ARL 1 \mathrm{ARL}_{1} . The proposed TS-c-chart has lower ARL 1 \mathrm{ARL}_{1} values for detecting small and moderate shifts in the mean number of non-conformities in control compared with FP-c, VSS-c, EWMA-c, and DS-c.

October 4, 2023

Measuring One-Sided Process Capability Index for Autocorrelated Data in the Presence of Random Measurement Errors

Kuntal Bera, M. Z. Anis

Page range: 95-107

Abstract

Many quality characteristics in manufacturing industry are of one sided specifications. The well-known process capability indices C P ⁢ U C_{PU} and C P ⁢ L C_{PL} are often used to measure the performance of such type of production process. It is usually assumed that process observations are independent and measurement system is free of errors. But actually in many industry it has been proven that auto-correlation is an inherent nature of the production process, especially for chemical processes. Moreover, even with the use of highly sophisticated advanced measuring instruments some amount of measurement error is always present in the observed data. Hence gauge measurement error also needs to be considered. In this paper we discuss some inferential properties of one-sided process capability indices for a stationary Gaussian process in the presence of measurement errors. As a particular case of a stationary Gaussian process, we discuss the case of a stationary AR ⁡ ( 1 ) \operatorname{AR}(1) process where measurement error follows an independent Gaussian distribution.

October 4, 2023

Enhancing Multivariate Control Charts for Individual Observations Using ROC Estimates

S. Vijayalakshmi, Nicy Sebastian, T. A. Sajesh

Page range: 109-118

Abstract

Classical Hotelling’s T 2 T^{2} control chart is frequently used for monitoring a multivariate process. The existence of outlying observations in the Phase I data used to determine the control limit can have a significant impact on the accuracy of such control charts, as is well known. Based on the robust reweighted orthogonalized comedian estimates, a robust multivariate quality control chart for individual observations is proposed in this study. Control limit of the proposed robust control chart is estimated by modelling the simulated quantiles for any sample size. A Simulation study has been conducted to examine the performance of the proposed method and compare it with the performances of the classical Hotelling T 2 T^{2} control chart, a robust control chart based on shrinkage reweighted estimator and two robust control charts based on the reweighted minimum covariance determinant estimator. The results demonstrate the effectiveness of the proposed method, even under high-dimensional settings or when the contamination in the Phase I data is high, with both independent and correlated variables. Performance of the proposed method is also illustrated by implementing in a real-world example

October 27, 2023

Time to Absorption in Markov Chains as a Mixture Distribution of Hypo-Exponential Distributions

Josh Hiller

Page range: 119-123

Abstract

We given an elementary proof that in a Markov chain with absorbing states, and positive probability of absorption at some time t > 0 {t>0} , time to absorption follows a mixture distribution of hypo-exponential random variables. We use this fact to show that early approximations of such a distribution yield the length of the shortest path from an initial state to an absorbing state. Thus different Markov chains with the same distance of shortest paths can yield identical first order approximations. Our work is motivated by the classical Armitage and Doll model of carcinogenesis.

Volume 38 (2023)

Volume 37 (2022)

Volume 36 (2021)

Volume 35 (2020)

Volume 34 (2019)

Volume 33 (2018)

Volume 32 (2017)

Volume 31 (2016)

Volume 30 (2015)

Volume 29 (2014)

Volume 28 (2013)

Volume 27 (2012)

Volume 26 (2011)

Volume 25 (2010)

Volume 24 (2009)

Volume 23 (2008)

Volume 22 (2007)

Volume 21 (2006)

Volume 20 (2005)

Volume 19 (2004)

Volume 18 (2003)

Volume 17 (2002)

Volume 16 (2001)

CiteScore	0.9	2022, Scopus (Elsevier B.V., 2023)
SCImago Journal Rank	0.201	2022, SJR (Scimago Lab, 2023; Data Source: Scopus)
Source Normalized Impact per Paper	0.473	2022, CWTS Journal Indicators (CWTS B.V., 2023; Data Source: Scopus)

More information about journal rankings

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Editorial

Editor-in-Chief

George P. Yanev
School of Mathematical and Statistical Sciences
The University of Texas Rio Grande Valley
1201 West University Drive
Edinburg, Texas 78539-2999
USA
Email: george.yanev@utrgv.edu

Founding Editor

Elart von Collani
Stochastikon GmbH
Schießhausstr. 15
97072 Würzburg
Germany
Email: collani@stochastikon.com

Associate Editors

Justin R. Chimka
Department of Industrial Engineering
University of Arkansas
4207 Bell Engineering Center
Fayetteville, AR 72701
USA
Email: jchimka@uark.edu

Boyan Dimitrov
Science and Mathematics Department
Kettering University
1700 West Third Avenue
Flint, MI 48504-4898
USA
Email: bdimitro@kettering.edu

Miguel González
Department of Mathematics
University of Extremadura
Avda. de Elvas, s/n
06071 Badajoz
Spain
Email: mvelasco@unex.es

Olgierd Hryniewicz
Systems Research Institute
Polish Academy of Sciences
Newelska 6
01-447 Warszawa
Poland
Email: hryniewi@ibspan.waw.pl

Vladimir K. Kaishev
Faculty of Actuarial Science and Insurance
Cass Business School, City University London
106 Bunhill Row
London EC1Y 8TZ
UK
Email: v.kaishev@city.ac.uk

Magdi Moustafa
The American University
P.O.Box 2511
Cairo 11511, Egypt
Email: mmostafa@aucegypt.edu

S.P. Mukherjee
Department of Statistics
University College of Science
35 Ballygunge Circular Rd.
Calcutta 700 019, India

Email: prof.mukherjee@gmail.com

Ken Nishina
Nagoya Institute of Technology
Gokiso-cho, Showa-ku
Nagoya, Aichi, 466-8555
Japan
Email: nishina@nitech.ac.jp

Erwin Saniga
Department of Business Administration
University of Delaware
Newark, Delaware 19716
USA
Email: sanigae@be.udel.edu

(Deutsch)

Online ISSN: 2367-2404
Print ISSN: 2367-2390
Type: Journal
Language: English
Publisher: De Gruyter
First published: April 1, 2001
Publication Frequency: 2 Issues per Year
Audience: mathematicians, scientists, economists as well as scholars from other disciplines