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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.16 No.4
- 발행연도
- 2017.12
- 수록면
- 455 - 464 (10page)
- DOI
- 10.7232/iems.2017.16.4.455
이용수
초록· 키워드
오류제보하기
First step of reconfigurable supply chain network developing is understanding of risk affects. Categorizing of risk module and results of risk events considered with gathering and analyzing of data from network design parameters. These obtained data have probability distribution that shows uncertainty in parameters. In the literatures supply chain risk categorized based on occurrences rate or frequency and period or duration time and also place of occurrence. Deal with uncertainty and complexity of risk, our proposed fuzzy method can solve these problems in two aspects. Also described a fuzzy based sampling method (FLHS) that developed and used in this paper can improve our results even more than previous works. This paper suggests a novel modelling and simulation method of fuzzy sampling and fuzzy analysis system to address the dynamic risks effects in the especially the consideration of uncertainty risk event system behavior in different operational conditions.
목차
ABSTRACT
1. INTRODUCTION
2. DESIGN A RECONFIGURABLE MULTI-AGENT HEALTHCARE SUPPLY CHAIN NETWORK UNDER RISK
3. PROPOSED FUZZY LHS SAMPLING
4. PROPOSED METHOD FOR FUZZY RISK
5. RESULTS AND EXPERIENCES
6. CONCLUSIONS AND FUTURE WORKS
REFERENCES
참고문헌 (13)
참고문헌 신청
Aqlana, F. and Lam, S. (2015), A fuzzy-based integrated framework for supply chain risk assessment, International Journal of Production Economics, 161, 54-63.
Ceryno, P., Scavarda, L., Klingebiel, K., and Yuzgulec, G. (2013), Supply chain risk management: A content analysis approach, International Journal of Industrial Engineering and Management (IJIEM), 4(3), 141- 150.
Feyzioglu, O., Buyukozkan, G., and Ersoy, M. S. (2007), Supply chain risk analysis with fuzzy cognitive maps, Industrial Engineering and Engineering Management, IEEE International Conference on, Sheffield.
Li, C., Ren, J., and Wang, H. (2016a), A system dynamics simulation model of chemical supply chain transportation risk management systems, Computers and Chemical Engineering, 89(9), 71-83.
Li, M., Du, Y., Wang, Q., Sun, C., Ling, X. and Yu, B. (2016b), Risk assessment of supply chain for pharmaceutical excipients with AHP-fuzzy comprehensive evaluation, Drug Development and Industrial Pharmacy, 42(4), 676-684.
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