• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.1-11
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• 2003

In this paper, we focus an optimal policy focus optimal class of (s, S) inventory control systems. To this end, we use the perturbation analysis and apply a stochastic optimization algorithm to minimize the average cost over a period. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. Our simulation results indicate that the optimal estimates of s and S obtained from a stochastic optimization algorithm are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and review period. Another directions involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).

• Structural Engineering and Mechanics
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• v.4 no.6
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• pp.703-715
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• 1996

In stochastic analysis, the randomness of the structural parameters is taken into consideration and the response variability is obtained in addition to the conventional (mean) response. In the present paper the structural response variability of plate structure is calculated using the weighted integral method and is compared with the results obtained by different methods. The stochastic field is assumed to be normally distributed and to have the homogeneity. The decomposition of strain-displacement matrix enabled us to extend the formulation to the stochastic analysis with the quadratic elements in the weighted integral method. A new auto-correlation function is derived considering the uncertainty of plate thickness. The results obtained in the numerical examples by two different methods, i.e., weighted integral method and Monte Carlo simulation, are in a close agreement. In the case of the variable plate thickness, the obtained results are in good agreement with those of Lawrence and Monte Carlo simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.283-287
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• 2010

Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. In the current study, some stochastic simulation approaches tackling some of the existing problems are discussed. The presented models are based on nonparametric techniques such as block bootstrapping, and K-nearest neighbor resampling (KNNR), and kernel density estimate (KDE). Three different types of the presented stochastic simulation models are (1) Pilot Gamma Kernel estimate with KNNR (a single site case) and (2) Enhanced Nonparametric Disaggregation with Genetic Algorithm (a disaggregation case). We applied these models to one of the most challenging and critical river basins in USA, the Colorado River. These models are embedded into the hydrological software package, Pros and cons of the models compared with existing models are presented through basic statistics and drought and storage-related statistics.

• Journal of Korean Society of Transportation
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• v.10 no.3
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• pp.7-20
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• 1992

The common cycle time for the linded signals is usually determined for the critical intersecion, just because the cpacity of a signalized intersection depends on the cycle time. This may not be optimal since the interactions between the flow and the spatial structure of the route or the area are disregarded in this case. It is common to separate the total delay incurred at signals into two parts, a deterministic or uniform delay and a stochastic or random delay. The deterministic delays and the stochastic delays on the artery particularly related to signal cycle time. For this purpose a microscopic simulation technique is used to evaluate deterministic delays, and a macroscopic simulation technique based on the principles of Markov chains is used to evaluate stochastic delays with over flow queue. As a result of investigating the relations between deterministic delays and cycle time in the various circumstances of spacing of signals and traffic volume. As for stochastic delays the resalts of comparisons of the macroscopic simulation and Newell's approximation with the microscopic simulation indicate that the former is valid for the degree of saturation less than 0.95 and the latter is for that above 0.95. Newell's argument that the total stochastic delay on an arterial is dominated by that at or caused by critical intersection is certified by the simulation experiments. The comprehensive analyses of the values of optimal cycle time with various conditions lead to a model. The cycle time determined by this model shows to be approximately 70% of that calculated by Webster's.

• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.111-122
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• 2012

Capacity planning plays an important role not only for master production plan but also for facility or layout design in shipbuilding. Product work breakdown structure, attributes of production resources, and production method or process data are associated in order to make the discrete event simulation model of shipyard layout plan. The production amount of each process and the process time is assumed to be stochastic. Based on the stochastic discrete event simulation model, the production capacity of each facility in shipyard is estimated. The stochastic model of product arrival time, process time and transferring time is introduced for each process. Also, the production capacity is estimated for the assumed master production schedule.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.20 no.5
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• pp.457-462
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• 1987

Stochastic stability criterias for ocean wave analysis add simulation are studied using the data simulated by the linear superposition method. To clarify the criterias, the effects of the simulation parameters on the variance of stochastic properties of ocean waves are investigated, and the stable conditions of the parameters are estimated through the comparative study on the stochastic properties of simulated waves and well-known ocean waves. The simulation parameters considered are high frequency cut-off, data length, and number and phase angle of component waves. Statistical characteristics analysed are wave height, period and steepness, and the formation of groups of higher waves, resonance periods, steeper higher waves and extreme run-length of the run.

• Steel and Composite Structures
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• v.9 no.6
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• pp.499-518
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• 2009

The present paper investigates the stochastic seismic responses of steel structure systems with Partially Restrained (PR) connections by using Perturbation based Stochastic Finite Element (PSFEM) method. A stiffness matrix formulation of steel systems with PR connections and PSFEM and MCS formulations of structural systems are given. Based on the formulations, a computer program in FORTRAN language has been developed, and stochastic seismic analyses of steel frame and bridge systems have been performed for different types of connections. The connection parameters, material and geometrical properties are assumed to be random variables in the analyses. The Kocaeli earthquake occurred in 1999 is considered as a ground motion. The connection parameters, material and geometrical properties are considered to be random variables. The efficiency and accuracy of the proposed SFEM algorithm are validated by comparison with results of Monte Carlo simulation (MCS) method.

• Wind and Structures
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• v.29 no.6
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• pp.389-403
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• 2019

In this study, two original spectral representations of stationary stochastic fields, say the continuous proper orthogonal decomposition (CPOD) and the frequency-wavenumber spectral representation (FWSR), are derived from the Fourier-Stieltjes integral at first. Meanwhile, the relations between the above two representations are discussed detailedly. However, the most widely used conventional Monte Carlo schemes associated with the two representations still leave two difficulties unsolved, say the high dimension of random variables and the incompleteness of probability with respect to the generated sample functions of the stochastic fields. In view of this, a dimension-reduction model involving merely one elementary random variable with the representative points set owing assigned probabilities is proposed, realizing the refined description of probability characteristics for the stochastic fields by generating just several hundred representative samples with assigned probabilities. In addition, for the purpose of overcoming the defects of simulation efficiency and accuracy in the FWSR, an improved scheme of non-uniform wavenumber intervals is suggested. Finally, the Fast Fourier Transform (FFT) algorithm is adopted to further enhance the simulation efficiency of the horizontal stochastic wind velocity fields. Numerical examplesfully reveal the validity and superiorityof the proposed methods.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.235-236
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• 2002

Tribological phenomena such as wear or transfer are influenced by various factors and have complicated behavior. Therefore, it is difficult to predict the behavior of the gribological phenomena because of their complexity. But, those tribological phenomena can be considered simply as to transfer micro material particles from the sliding interface. Then, we proposed the numerical simulation method for tribological phenomena such as wear of transfer using stochastic process models. This numerical simulation shows the change of the 3-D surface topography. In this numerical simulation, initial 3-D surface toughness data are generated by the method of non-causal 2-D AR (autoregressive) model. Processes of wear and transfer for some generated initial 3-D surface data are simulated. Simulation results show successfully the change of the 3-D surface topography.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.71-94
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• 2009

In this paper a procedure for Monte Carlo simulation of univariate stationary stochastic processes with the aid of neural networks is presented. Neural networks operate model-free and, thus, circumvent the need of specifying a priori statistical properties of the process, as needed traditionally. This is particularly advantageous when only limited data are available. A neural network can capture the "pattern" of a short observed time series. Afterwards, it can directly generate stochastic process realizations which capture the properties of the underlying data. In the present study a simple feed-forward network with focused time-memory is utilized. The proposed procedure is demonstrated by examples of Monte Carlo simulation, by synthesis of future values of an initially short single process record.