TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
Powered by:DOICrossrefiThenticate

The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to: • Information Sciences • Optimization Sciences • Control Theory • Operational Research • Decision Sciences • Information Theory • Information Technology • Computer Networks and Communications • Mathematical Programming • Modelling and Simulation • Database Management • Applications to Engineering Sciences • Applications to Technology

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Optimal quality investment and specification limits settings with 100% inspection and sampling inspection for quality protection

* ,

* Corresponding author · click or hover a name for details

pp. 1345–1358Vol. 47Issue 4April 2026DOI: 10.47974/JIOS-1424XML
Received:
08 Feb 2023
Published Online:
01 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1424
Pages:
1345–1358

Abstract

Shin et al. [2] proposed the process parameters and tolerance designs model. Their model firstly obtained the process mean and standard deviation. Then the tolerance model is formulated for obtaining the optimal tolerance.  Product inspection is a short-term method for assuring the shipment quality. One should consider a long-term method for improving quality, e.g., quality investment. In this paper, the authors address the extension of Shin et al.’s [2] tolerance model with quality investment.

Keywords

Subject Classifications

60E05 Probability distributions: general theory

References

[1] G. Taguchi, Introduction to Quality Engineering, Asian Productivity Organization, Tokyo (1986).[2] S. Shin, P. Kongsuwon, and B. R. Cho, “Development of the parametric tolerance modeling and optimization schemes and cost-effective solutions,” European Journal of Operational Research, vol. 207, pp. 1728-1741 (2010).[3] P. L. Goethals and B. R. Cho, “The development of multi-response experimental designs for process parameter optimization,” International Journal of Quality & Reliability Management, vol. 28, pp. 628-648 (2011).[4] P. L. Goethals and B. R. Cho, “The optimal process mean problem: Integrating predictability and profitability into an experimental factor space,” Computers & Industrial Engineering, vol. 62, pp. 851-869 (2012).[5] G. L. Boylan and B. R. Cho, “Comparative studies on the high-variability embedded robust parameter design from the perspective of estimators,” Computers & Industrial Engineering, vol. 164, pp. 442-452 (2013).[6] G. L. Boylan, P. L. Goethals, and B. R. Cho, “Robust parameter design in resource-constrained environments: An investigation of trade-offs between costs and precision within variable processes,” Applied Mathematical Modelling, vol. 37, pp. 2394-2416 (2013).[7] S. A. Raza, F. C. Abdullakutty, S. Rathinam, and S. M. Govindaluri, “Multi-objective framework for process mean selection and price differentiation with leakage effects under price-dependent stochastic demand,” Computers & Industrial Engineering, vol. 127, pp. 698-708 (2019).[8] V. E. Kane, “Process capability indices,” Journal of Quality Technology, vol. 18, pp. 41-52 (1986).[9] L. K. Chan, S. W. Cheng, and F. A. Spring, “A new measure of process capability: Cpm,” Journal of Quality Technology, vol. 30, pp. 162-175 (1988).[10] R. A. Boyles, “The Taguchi capability index,” Journal of Quality Technology, vol. 23, pp. 107-126 (1991).[11] W. L. Pearn, S. Kotz, and N. L. Johnson, “Distributional and inferential properties of process capability indices,” Journal of Quality Technology, vol. 24, pp. 216-231 (1992).[12] H. Wu and K. Govindaraju, “Computer-aided variables sampling inspection plans for compositional proportions and measurement error adjustment,” Computers & Industrial Engineering, vol. 72, pp. 239-246 (2014).[13] B. M. Hsu, T.-C. Wang, and M.-H. Shu, “Lot-dependent sampling plans for qualifying long-term production capability with a one-sided specification,” Computers & Industrial Engineering, vol. 146, p. 106583 (2020).[14] C. H. Chen and C. Y. Chou, “Economic design of product and process parameters under the specified process capability index,” Quality Technology and Quantitative Management, vol. 15, pp. 686-701 (2018).[15] C. H. Chen and C. Y. Chou, “Optimal process mean setting by considering sampling inspection and process capability,” Journal of Information and Optimization Sciences, vol. 41, pp. 1795-1801 (2020).[16] A. Banihashemi, M. S. F. Nezhad, and A. Amiri, “A new approach in the economic design of acceptance sampling plans based on process yield index and Taguchi loss function,” Computers & Industrial Engineering, vol. 159, p. 107155 (2021).[17] D. Bose and A. Guha, “Economic production lot sizing under imperfect quality, on-line inspection and inspection error: Full vs. sampling inspection,” Computers & Industrial Engineering, vol. 160, p. 107565 (2021).
Views: 43Downloads: 5Citations: 0