• 제목/요약/키워드: stochastic simulation.

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확률적 네트워크 Simulation 방법을 이용한 프로젝트의 위험분석모델 (A Stochastic Network Simulation Model for Project Risk Analysis)

  • 황흥석
    • 한국시뮬레이션학회:학술대회논문집
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    • 한국시뮬레이션학회 2000년도 추계학술대회 논문집
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    • pp.16-21
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    • 2000
  • 본 연구는 대형 프로젝트의 위험분석을 위한 확률적 Network 시뮬레이션모델의 연구로서 Simulation방법으로 프로젝트의 성공 및 실패확률을 산정 하였다. 프로젝트의 주요 불확실성 요소(Uncertainty Factors)인 프로젝트의 수행기간(Time), 비용(Cost) 및 성과(Performance) 등의 계획은 실패 없이 추진되어야 하는 것이 중요하다. 연구 개발 및 신기술개발과 같이 대형 프로젝트의 경우, 그 성과 달성의 위험(Risk)성은 매우 크며 이러한 위험 예측 및 분석이 프로젝트의 성공적인 수행을 위하여 매우 중요 시 된다. 본 연구에서는 이를 위한 위험분석(Risk Analysis)의 방법으로 일반적으로 쉽게 사용할 수 있는 위험요인법(Risk Factor Analysis)과 확률적 Network 시뮬레이션모델을 제시하였으며 또한 이를 위한 Simulation프로그램을 개발하였으며 이를 신 기술개발 프로젝트에 응용하는 과정을 보였다. 본 연구에서 개발된 관련 프로그램을 보완 할 경우 대형 프로젝트의 각종 의사결정 시에 매우 유용하게 활용될 수 있으리라 생각된다.

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Stochastic Programming for the Optimization of Transportation-Inventory Strategy

  • Deyi, Mou;Xiaoqian, Zhang
    • Industrial Engineering and Management Systems
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    • 제16권1호
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    • pp.44-51
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    • 2017
  • In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

Direct implementation of stochastic linearization for SDOF systems with general hysteresis

  • Dobson, S.;Noori, M.;Hou, Z.;Dimentberg, M.
    • Structural Engineering and Mechanics
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    • 제6권5호
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    • pp.473-484
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    • 1998
  • The first and second moments of response variables for SDOF systems with hysteretic nonlinearity are obtained by a direct linearization procedure. This adaptation in the implementation of well-known statistical linearization methods, provides concise, model-independent linearization coefficients that are well-suited for numerical solution. The method may be applied to systems which incorporate any hysteresis model governed by a differential constitutive equation, and may be used for zero or non-zero mean random vibration. The implementation eliminates the effort of analytically deriving specific linearization coefficients for new hysteresis models. In doing so, the procedure of stochastic analysis is made independent from the task of physical modeling of hysteretic systems. In this study, systems with three different hysteresis models are analyzed under various zero and non-zero mean Gaussian White noise inputs. Results are shown to be in agreement with previous linearization studies and Monte Carlo Simulation.

A Study on the Stochastic Finite Element Method for Dynamic Problem of Nonlinear Continuum

  • Wang, Qing;Bae, Dong-Myung
    • Journal of Ship and Ocean Technology
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    • 제12권2호
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    • pp.1-15
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    • 2008
  • The main idea of this paper introduce stochastic structural parameters and random dynamic excitation directly into the dynamic functional variational formulations, and developed the nonlinear dynamic analysis of a stochastic variational principle and the corresponding stochastic finite element method via the weighted residual method and the small parameter perturbation technique. An interpolation method was adopted, which is based on representing the random field in terms of an interpolation rule involving a set of deterministic shape functions. Direct integration Wilson-${\theta}$ Method was adopted to solve finite element equations. Numerical examples are compared with Monte-Carlo simulation method to show that the approaches proposed herein are accurate and effective for the nonlinear dynamic analysis of structures with random parameters.

공정 시뮬레이션 출력 변수의 확률분포 계산 알고리즘 (An Algorithm for Calculation of Probability Distributions of Output Variables in Process Simulation)

  • 최수형
    • 제어로봇시스템학회논문지
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    • 제8권10호
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    • pp.847-850
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    • 2002
  • Stochastic process analysis is often based on Monte Carlo simulations. As a more rigorous alternative, a deterministic algorithm based on numerical integration is proposed in this paper. which calculates the probability distributions of dependent random variables using the results of simulation with grid points of independent random variables. For performance evaluation, the proposed algorithm is applied to an example problem which can be analytically solved. and the result is compared with that of Monte Carlo simulation. The proposed algorithm is suitable for general process simulation problems with a few independent random variables, and expected to be applicable to areas such as safety analysis and quality control.

PRICING FORWARD-FUTURES SPREAD BASED ON COPULAS WITH STOCHASTIC SIMULATION

  • Pu, Yuqi;Kim, Seki
    • 한국수학교육학회지시리즈B:순수및응용수학
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    • 제21권1호
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    • pp.77-93
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    • 2014
  • This paper focuses on computational contractual distinctions as an explanation for the spread between a forward contract and a similar futures contract which is derived and investigated. We evaluate this spread by constructing a time series model, which was established based on copula functions, and also show that the forward-futures spread is more significant for long maturity.

DIGITAL OPTION PRICING BASED ON COPULAS WITH STOCHASTIC SIMULATION

  • KIM, M.S.;KIM, SEKI
    • 한국수학교육학회지시리즈B:순수및응용수학
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    • 제22권3호
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    • pp.299-313
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    • 2015
  • In this paper, we show the effectiveness of copulas by comparing the correlation of market data of year 2010 with those of years 2006-2009 and investigate copula functions as pricing methods of digital and rainbow options through real market data. We propose an accurate method of pricing rainbow options by using the correlation coefficients obtained from the copula functions depending on strike prices between assetes instead of simple traditional correlation coefficients.

복합 무기체계 비용${\cdot}$효과 분석 방법론 연구 (A Stochastic Simulation Model for Integrated Weapon System Design and Performance Evaluation)

  • 황흥석
    • 한국국방경영분석학회지
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    • 제18권2호
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    • pp.23-43
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    • 1992
  • A Stochastic simulation model for optimal design and evaluation of the integrated weapon systems under the consideration of the RAM (reliability, availability and maintainability) and life cycle cost (LCC) was developped. This model is supposed to satisfy a need regarding the methodology of optimaldesign and performance evaluation for both developpers and users of weapon systems.

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추계학적 강지진동 모사를 위한 Q의 주파수 의존 특성에 대한 연구 (Study on frequency dependency of Q for Stochastic Strong Ground Motion Simulation)

  • 연관희;박동희;장천중
    • 한국지진공학회:학술대회논문집
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    • 한국지진공학회 2003년도 춘계 학술발표회논문집
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    • pp.77-84
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    • 2003
  • For stochastic strong ground motion simulation, frequency-dependent Q model (= $Q_{o}$ $f^{η}$) were evaluated for major geographical blocks according to the epicentral distance ranges by using a lateral Q tomography technique. The inversed Q results were used to qualitatively identify seismic albedos of each Q blocks and were compared with the previous Q studies. In addition, a functional Q model calibrated to the low frequency spectra of local earthquakes were suggested especially for use in analysing large and distant regional earthquake events.s.

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Numerical Simulation of Buoyant flume Dispersion in a Stratified Atmosphere Using a Lagrangian Stochastic Model

  • Kim, Hyun-Goo;Noh, Yoo-Jeong;Lee, Choung-Mook;Park, Don-Bum
    • Journal of Mechanical Science and Technology
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    • 제17권3호
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    • pp.440-448
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    • 2003
  • In the present paper, numerical simulations of buoyant plume dispersion in a neutral and stable atmospheric boundary layer have been carride out. A Lagrangian Stochastic Model (LSM) with a Non-Linear Eddy Viscosity Model (NLEVM) for turbulence is used to generate a Reynolds stress field as an input condition of dispersion simulation. A modified plume-rise equation is included in dispersion simulation in order to consider momentum effect in an initial stage of plume rise resulting in an improved prediction by comparing with the experimental data. The LSM is validated by comparing with the prediction of an Eulerian Dispersion Model (EDM) and by the measured results of vertical profiles of mean concentration in the downstream of an elevated source in an atmospheric boundary layer. The LSM predicts accurate results especially in the vicinity of the source where the EDM underestimates the peak concentration by 40% due to inherent limitations of gradient diffusion theory. As a verification study, the LSM simulation of buoyant plume dispersions under a neutral and stable atmospheric condition is compared with a wind-tunnel experiment, which shows good qualitative agreements.