• Journal of the Korea Society for Simulation
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• v.9 no.1
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• pp.93-108
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• 2000

The stochastic manufacturing system has one or more random variables as inputs that lead to random outputs. Since the outputs are random, they can be considered only as estimates of the true characteristics of the system. These estimates could greatly differ from the corresponding real characteristics for the system. Multiple replications are necessary to get reliable information on the system and output data should be analyzed to get optimal solution. It requires too much computation time practically, In this paper a GA method, named Stochastic Genetic Algorithm(SGA) is proposed and tested to find the optimal solution fast and efficiently by reducing the number of replications.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.395-398
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• 2011

This paper introduces stochastic cash-flow modeling integrated with simulation based scheduling. The system makes use of CPM schedule data exported from commercial scheduling software, computes the best fit probability distribution functions (PDFs) of historical activity durations, assigns the PDFs identified to respective activities, simulates the schedule network, computes the deterministic and stochastic project cash-flows, plots the corresponding cash flow diagrams, and estimates the best fit PDFs of overdraft and net profit of a project. It analyzes the effect of different distributions of activity durations on the distribution of overdrafts and net profits, and improves reliability compared to deterministic cash flow analysis.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.4
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• pp.17-26
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• 2001

In order to estimate peak ground motions and frequency characteristics of strong ground motions in southern korea, we employed the stochastic simulation method with the moment magnitude(M$_{w}$) and the hypocentral distance(R). We estimated same input parameters that account for specific properties of source and propagation processes, and applied them to the stochastic simulation method. The stress drop($\Delta$$\sigma$) of 100-bar was estimated considering results of research in ENA, China, and southern korea. The attenuation parameter x was calculated by analyzing 57 seismograms recorded from September 1996 to October 1997 and the estimation result of the attenuation parameter x is 0.00112+0.000224 R where R is hypocenter distance. We estimated strong ground motion relations using the stochastic simulation method with suitable input parameters(e.g. $\Delta$$\sigma$, x, and so on). At last, we derived relations between hypocentral distances and ground motions(seismic attenuation equation) using results of the stochastic prediction.esults of the stochastic prediction.n.

• Journal of Industrial Technology
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• v.22 no.B
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• pp.3-12
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• 2002

Monte-Carlo simulation technique has advantages over deterministic simulation in various engineering analysis since Monte-Carlo simulation can take into consideration of scattering of various design variables, which is inherent characteristics of physical world. In this work, Monte-Carlo simulation of steady-state cornering behavior of a truck with design variables like hard points and busing stiffness. The purpose of the simulation is to improve understeer gradient of the truck, which exhibits a small amount of instability when the lateral acceleration is about 0.4g. Through correlation analysis, design variables that have high impacts on the cornering behavior were selected, and significant performance improvement has been achieved by appropriately changing the high impact design variables.

• Journal of Construction Engineering and Project Management
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• v.3 no.3
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• pp.23-34
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• 2013

As climate change and environmental pollution become one of the biggest global issues today, new renewable energy, especially solar photovoltaic (PV) system, is getting great attention as a sustainable energy source. However, initial investment cost of PV system is considerable, and thus, it is crucial to predict electricity generation accurately before installation of the system. This study analyzes the loss ratio of solar photovoltaic electricity generation from the actual PV system monitoring data to predict electricity generation more accurately in advance. This study is carried out with the following five steps: (i) Data collection of actual electricity generation from PV system and the related information; (ii) Calculation of simulation-based electricity generation; (iii) Comparative analysis between actual electricity generation and simulation-based electricity generation based on the seasonality; (iv) Stochastic approach by defining probability distribution of loss ratio between actual electricity generation and simulation-based electricity generation ; and (v) Case study by conducting Monte-Carlo Simulation (MCS) based on the probability distribution function of loss ratio. The results of this study could be used (i) to estimate electricity generation from PV system more accurately before installation of the system, (ii) to establish the optimal maintenance strategy for the different application fields and the different season, and (iii) to conduct feasibility study on investment at the level of life cycle.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.375-385
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• 2013

As climate change and environmental pollution become one of the biggest global issues today, new renewable energy, especially solar photovoltaic (PV) system, is getting great attention as a sustainable energy source. However, initial investment cost of PV system is considerable, and thus, it is crucial to predict electricity generation accurately before installation of the system. This study analyzes the loss ratio of solar photovoltaic electricity generation from the actual PV system monitoring data to predict electricity generation more accurately in advance. This study is carried out with the following five steps: (i) Data collection of actual electricity generation from PV system and the related information; (ii) Calculation of simulation-based electricity generation; (iii) Comparative analysis between actual electricity generation and simulation-based electricity generation based on the seasonality; (iv) Stochastic approach by defining probability distribution of loss ratio between actual electricity generation and simulation-based electricity generation ; and (v) Case study by conducting Monte-Carlo Simulation (MCS) based on the probability distribution function of loss ratio. The results of this study could be used (i) to estimate electricity generation from PV system more accurately before installation of the system, (ii) to establish the optimal maintenance strategy for the different application fields and the different season, and (iii) to conduct feasibility study on investment at the level of life cycle.

• Geomechanics and Engineering
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• v.7 no.5
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• pp.525-537
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• 2014

The typical design of ground improvement with prefabricated vertical drains (PVD) and surcharge preloading involves a series of deterministic analyses using averaged or mean soil properties for the various combination of the PVD spacing and surcharge preloading height that would meet the criteria for minimum consolidation time and required degree of consolidation. The optimum design combination is then selected in which the total cost of ground improvement is a minimum. Considering the variability and uncertainties of the soil consolidation parameters, as well as considering the effects of soil disturbance (smear zone) and drain resistance in the analysis, this study presents a stochastic cost optimization of ground improvement with PVD and surcharge preloading. Direct Monte Carlo (MC) simulation and importance sampling (IS) technique is used in the stochastic analysis by limiting the sampled random soil parameters within the range from a minimum to maximum value while considering their statistical distribution. The method has been verified in a case study of PVD improved ground with preloading, in which average results of the stochastic analysis showed a good agreement with field monitoring data.

• Wind and Structures
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• v.16 no.5
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• pp.433-455
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• 2013

A phase delay spectrum model towards the representation of spatial coherence of stochastic wind fields is proposed. Different from the classical coherence functions used in the spectral representation methods, the model is derived from the comprehensive description of coherence of fluctuating wind speeds and from the thorough analysis of physical accounts of random factors affecting phase delay, building up a consistent mapping between the simulated fluctuating wind speeds and the basic random variables. It thus includes complete probabilistic information of spatial stochastic wind fields. This treatment prompts a ready and succinct scheme for the simulation of fluctuating wind speeds, and provides a new perspective to the accurate assessment of dynamic reliability of wind-induced structures. Numerical investigations and comparative studies indicate that the developed model is of rationality and of applicability which matches well with the measured data at spatial points of wind fields, whereby the phase spectra at defined datum mark and objective point are feasibly obtained using the numerical scheme associated with the starting-time of phase evolution. In conjunction with the stochastic Fourier amplitude spectrum that we developed previously, the time history of fluctuating wind speeds at any spatial points of wind fields can be readily simulated.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.437-437
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• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.57-67
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• 1994

For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.