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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.16 No.2
- 발행연도
- 2017.6
- 수록면
- 195 - 204 (10page)
- DOI
- 10.7232/iems.2017.16.2.195
이용수
초록· 키워드
오류제보하기
In this article, a multiple dependent state repetitive (MDSR) sampling plan is developed to choose a more capable process by comparing two suppliers’ processes. The proposed MDSR plan is based on the ratio of two process yield indices based on the estimated linear profiles. The linear profile for both processes is estimated by a regression model with a single explanatory variable having the specified number of levels. The operating characteristic function of the proposed plan will be derived. The plan parameters related to the decision rule as well as the sample size are determined by minimizing the average sample number while satisfying the producer’s and the consumer’s risks.
목차
ABSTRACT
1. INTRODUCTION
2. PROCESS YIELD INDEX FOR A LINEAR PROFILE
3. PROPOSED MDSR SAMPLING PLAN BASED ON A RATIO OF TWO PROCESS YIELD INDICES
4. COMPARATIVE STUDY
5. AN ILLUSTRATIVE EXAMPLE
6. CONCLUSION
REFERENCES
참고문헌 (18)
참고문헌 신청
Amiri, A., Zou, C., and Doroudyan, M. H. (2014), Monitoring correlated profile and multivariate quality characteristics, Quality and Reliability Engineering International, 30(1), 133-142.
Aslam, M., Azam, M., and Jun, C. H. (2013a), Multiple dependent state sampling plan based on process capability index, Journal of Testing and Evaluation, 41(2), 340-346.
Aslam, M., Yen, C.-H., Chang, C.-H., and Jun, C.-H. (2013b), Multiple states repetitive group sampling plans with process loss consideration, Applied Mathematical Modelling, 37(20-21), 20-21.
Aslam, M., Azam, M., and Jun, C. H. (2016), Multiple dependent state repetitive group sampling plan for burr xii distribution, Quality Engineering, 28(2), 231-237.
Balamurali, S., Park, H., Jun, C. H., Kim, K. J., and Lee, J. (2005), Designing of variables repetitive group sampling plan involving minimum average sample number, Communications in Statistics- Simulation and Computation, 34(3), 799-809.
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