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논문 기본 정보

자료유형
학술저널
저자정보
Saman Siadati (Islamic Azad University) Mohammad Jafar Tarokh (K. N. Toosi University of Technology) Rassoul Noorossana (Iran University of Science and Technology)
저널정보
대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.17 No.2
발행연도
2018.6
수록면
294 - 301 (8page)
DOI
10.7232/iems.2018.17.2.294

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초록· 키워드

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Considering the effects of risk on supply chain in healthcare industry, we must provide a mathematical model based on the risk to re-design the supply chain network, which is a part of the optimization module, random sampling methods use. One of the objectives for applying sampling methods is to determine the best method (by reducing the variance and computational time) for different sizes. The large number of random parameters of the objective function value led to very high variance that required using methods for reducing the variance. In this research, our approach to handle risk analysis problems in mean approximation is using traditional sampling method namely Latin hypercube sampling. However, to reduce error in correlations between variables, it is proposed to perform a fuzzy method on the intervals to eliminate uncertainty in statistical values. Limitations in hypercube sampling will be discussed and numerical results involving a FLHS are presented and compared with Monte Carlo, simple LHS and other types of LHS. We show that the proposed method can affect the precision of mean and variance values.

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ABSTRACT
1. INTRODUCTION
2. DEFININTIONS
3. COMPARISIONS LHS WITH MONTE CARLO
4. FUZZY LHS SAMPLING
5. PROVING OPTIMALITY
6. CONCLUSIONS
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UCI(KEPA) : I410-ECN-0101-2018-530-003116674