• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.133-142
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• 2008

The Emergency Department (ED) is an integral part of hospitals. Admissions from the ED account for a significant proportion for a hospital's activity. Ensuring a timely and efficient flow of patients through the ED is crucial for optimising patient care. In recent years, ED overcrowding and its impact on patient flow has become a major issue facing the health sector. Simulation is rapidly becoming a tool of choice when examining hospital systems due to its capacity to involve numerous factors and interactions that impact the system. An analytical simulation model is used to investigate potential impacts by changing the following aspects of ED (physical layouts; number of beds; number and rate of patient arrivals; acuity of illness or injury of patients; access to radiology and pathology services; hospital staffing arrangements; and access to inpatient beds). Results of a significant numerical investigation at a hospital are also presented.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.169-173
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• 2015

The linear scheduling method or line-of-balance (LOB) is a popular choice for projects that involve repetitive tasks during project execution. The method, however, produces deterministic schedule that does not convey a range of potential project outcomes under uncertainty. This results from the fact the basic scheduling parameters such as crew production rates are estimated to be deterministic based on single-point value inputs. The current linear scheduling technique, therefore, lacks the capability of reflecting the fluctuating nature of the project operation. In this paper the authors address the issue of how the variability of operation and production rates affects schedule outcomes and show a more realistic description of what might be a realistic picture of typical projects. The authors provide a solution by providing a more effective and comprehensive way of incorporating the crew performance variability using a Monte Carlo simulation technique. The simulation outcomes are discussed in terms of how this stochastic approach can overcome the shortcomings of the conventional linear scheduling technique and provide optimum schedule solutions.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.460-463
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• 2000

Fuzzy logic control(FLC) has been studied extensively and has been applied in various applications. The most popular control strategy takes the Fuzzy Proportional-Integral(FPI) form while systematic methods have been developed to derive the fuzzy rules and membership functions the choice of the scaling factors remains an open problem, In this paper an analytical FPI scaling factor determining method is derived based on the functional equivalence of the PI and FPI controllers. Simulation have been carried out with a brushless DC motor drive system as test-bed the obtained results drive system as test-bed the obtained results have verified that the derived method is applicable to both the initial choice and further tuning of the FPI scaling factors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1633-1637
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• 2009

We propose a new technique for sidelobe suppression in orthogonal frequency division multiplexing (OFDM) systems. Sidelobe suppression is an essential technique to design OFDM based overlay system. The proposed technique is based on the combination of the multiple choice sequence (MCS) with the conventional windowing of OFDM signal in time domain. The MCS is choosing the one sequence which has lowest power in sidelobes from the produced set of sequences. The main advantage of proposed technique is that it fully utilizes the available bandwidth to transmit data. Simulation results show that by combining MCS with conventional windowing technique, the sidelobes in OFDM system can be significantly reduced

• Korean Management Science Review
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• v.29 no.2
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• pp.91-103
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• 2012

Revenue management problems originated in the 1970's in the context of the airline industry have been successfully introduced in airline industries. It has started on the capacity control by booking classes for available seats, and has been recognized as a powerful tool to maximize the total revenue. Changing customer behavior and airline market environments, however, has required a new mechanism for improving the revenue. Dynamic pricing is one of innovative tools which is to adjust prices according to the market status. In this paper, we consider a dynamic pricing and seat control problem for discrete time horizon. The problem can be modeled as a stochastic programming problem. Applying the linear approximation technique and given the price set for each time, we suggest a mixed Integer Programming model to solve our problem efficiently. From the simulation results, we can find our model makes good performance and can be expanded to other comprehensive problems.

• Analytical Science and Technology
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• v.8 no.4
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• pp.707-714
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• 1995

Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonstrates its excellent utility for the complex depth profile system. It includes the stable restoration of the depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.31-35
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• 2001

Simulation --- The ideal tool for BPR. Work now and CASE tools are static modeling tools. Based on our own customers surveys, we have discovered that the use of process modeling tools thus far has focused on modeling the current(What-Is) state of a business. We have found that 90 percent of reengineering projects, the modeling tools of choice have been flowcharting tools. Static models offer help in understanding the overall nature of an existing process. However, static models can not really help you see the step by step motions towards completion of your goals. In static modeling, you see two pictures in time, usually taken at the current state and final state models of your reengineering project. Static models are usually not object oriented, therefore can not show facility or office layout and movement of entities and objects throughout the facility. However, this does not mean that static modeling does not have its application nor add value to the user as in a few success stories. Simulation helps the team analyze the complex aspects of the project. Many times a plan that looks good on paper might turn out entirely different when put into action. Therefore, simulation helps you look at how situations might work before actual implementation. In particular, computer simulation models help you view a reengineered condition before they are rolled-out. Items such as a lead time and resource allocation.

• Environmental and Resource Economics Review
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• v.21 no.2
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• pp.237-269
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• 2012

This study applies a discrete-continuous choice model to a national survey data set of automobile uses to investigate the potential impacts of a bonus-malus system for new cars in Korea. Not only the impacts on the discrete choice of automobile type and class but also those on the continuous decision making of car operation are analyzed. The characteristics of automobiles and individuals that determine car choice and operation are identified. The simulation based on the estimation result shows that an appropriately designed bonus-malus system can induce a reduction in energy consumption and $CO_2$ emission substantially without additional government expenditure.

• Environmental and Resource Economics Review
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• v.16 no.3
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• pp.683-699
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• 2007

This study applied the choice experiment (CE) method to measure values of statistical lives from multi-attributed mortality risk reduction choices. The four characteristics of mortality risk (i.e. cause of death, voluntariness of mortality risk, timing of death, magnitude of mortality risk reduction) are utilized to design the alternatives of choice sets. The estimation results for the multinomial logit model show that individuals are willing to pay 27,930 won per year for a change from the status quo to a $\frac{1}{100}$ mortality risk reduction for 10 years, 116,773 won per year for mortality risk reduction associated with adults, 97,682 won per year for voluntary mortality risk reduction, 77,234 won per year for involuntary mortality risk reduction. There were several estimates of VSL related to different attributes of mortality risk. The mean VSLs of infant/child/young adult ranged from 1,165 million won to 1,367 million won. The mean VSLs ranged from 1,631 million won to 1,833 million won for adult, and were between 1,128 million won and 1,330 million won for old person.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.755-767
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• 1997

Analyzing autocorrelated data set is still an open problem. Developing on easy and efficient method for severe positive correlated data set, which is common in simulation output, is vital for the simulation society. Bootstrap is on easy and powerful tool for constructing non-parametric inferential procedures in modern statistical data analysis. Conventional bootstrap algorithm requires iid assumption in the original data set. Proper choice of resampling units for generating replicates has much to do with the structure of the original data set, iid data or autocorrelated. In this paper, a new bootstrap resampling scheme is proposed to analyze the autocorrelated data set : the Threshold Bootstrap. A thorough literature search of bootstrap method focusing on the case of autocorrelated data set is also provided. Theoretical foundations of Threshold Bootstrap is studied and compared with other leading bootstrap sampling techniques for autocorrelated data sets. The performance of TB is reported using M/M/1 queueing model, else the comparison of other resampling techniques of ARMA data set is also reported.