• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.233-242
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• 2014

A magnetically levitated vehicle (Maglev) system is under commercialization as a new transportation system in Korea. The Maglev is operated by an unmanned automatic control system. Therefore, the plan of train operation should be carefully established and validated in advance. In general, when making a train operation plan, statistically predicted traffic data is used. However, a traffic wave often occurs in real train service, and demand-driven simulation technology is required to review a train operation plan and service quality considering traffic waves. We propose a method and model to simulate Maglev operation considering continuous demand changes. For this purpose, we employed a discrete event model that is suitable for modeling the behavior of railway passenger transportation. We modeled the system hierarchically using discrete event system specification (DEVS) formalism. In addition, through implementation and an experiment using the DEVSim++ simulation environment, we tested the feasibility of the proposed model. Our experimental results also verified that our demand-driven simulation technology can be used for a priori review of train operation plans and strategies.

• Proceedings of the Technology Innovation Conference
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• 2003.02a
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• pp.173-190
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• 2003

Demand forecasting is essential in establishing national and corporate strategy as well as the management of their resource. We forecast demand for multi-generation product using discrete choice model combining diffusion model The discrete choice model generally captures consumers'valuation of the product's qualify in the framework of a cross-sectional analysis. We incorporate diffusion effects into a discrete choice model in order to capture the dynamics of demand for multi-generation products. As an empirical application, we forecast demand for worldwide DRAM (dynamic random access memory) and each of its generations from 1999 to 2005. In so doing, we use the method of 'Technological Forecasting'for DRAM Density and Price of the generations based on the Moore's law and learning by doing, respectively. Since we perform our analysis at the market level, we adopt the inversion routine in using the discrete choice model and find that our model performs well in explaining the current market situation, and also in forecasting new product diffusion in multi-generation product markets.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.59-65
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• 1988

This paper discusses the problem of coordinating aggregate planning and production schedules, minimizing the combined set-up inventory and capacity costs. In this study, by using the relation of fixed production quantity and the number of set-up we develop a heuristirc procedure of solving the discrete demand, fixed production quantity, variable capacity problem. First, we obtain the trade-off between set-up cost and capacity cost, then search the point minimizing the combined inventory and capacity costs.

• Management Science and Financial Engineering
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• v.17 no.1
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• pp.65-78
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• 2011

In the continuous review (Q, r) model one continuously monitors inventory level and places a replenishment order when the inventory position reaches the reorder point. In many business practices, however, inventory decreases in a discrete fashion. As a result, replenishment orders are usually placed after the inventory position gets far below the reorder point. This makes a chance of shortage more likely and the service level lower than designed. Such a discrepancy can be compensated for by raising the reorder point to some extent. The question is how much the reorder point should be raised in order to compensate for a potential shortage. In this study, we present experimental analyses for this question. Regression models are also proposed for on-site use.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.281-289
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• 2008

In this study, a discrete-event simulation model is developed to estimate load factors and the corresponding revenues under different pricing and dispatching policies. The model has been employed to forecast the inbound and the outbound air cargo demands of the major Northeastern Chinese cities, and the simulation results were compared to the actual demands obtained from real-life airline operations. The statistical analysis confirms that the simulation model is able to provide accurate estimates for air cargo demands, and thus, the model may be employed to be a useful tool for air cargo demand management.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.615-625
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• 2008

A single item order level inventory model for perishable products is considered in which a constant fraction of on hand inventory spoils per unit time. Demand linearly depends on time. The fluctuation of demand is taken into account to determine minimum total cost of the system. Both discrete and continuous fluctuations are considered. The model is developed and solved analytically for infinite time horizon. A numerical example is presented for finite time horizon. Sensitivity analysis of the model is carried out.

• Environmental and Resource Economics Review
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• v.23 no.3
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• pp.409-434
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• 2014

This paper estimates household demand for electricity using a micro-level household expenditure data set. A two-stage estimation method where the endogenous block price estimates are obtained from a discrete block choice model is used. This method successfully identifies a downward sloping conditional demand function with the data, while both the usual two-stage method with instrumental variable estimation and the Hewitt-Hanemann discrete-continuous model fail to do that. The paper simulates the impacts of two hypothetical pricing reforms that reduce the number of blocks and make the price gap smaller. It is shown that the reform may increase the overall consumer benefit, but is regressive.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.89-100
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• 2011

The MAGLEV (Magnetically Levitated Vehicle) system, which is under commercialization as a new transportation system in Korea, is operated by means of unmanned automatic control system. Therefore the plan of train operation should be carefully established and validated in advance. In general, when making the train operation plan, the statistically predicted traffic data is used. However, traffic wave can occur when real train service is operated, and the demand-driven simulation technology is required to review train operation plans and service qualities considering traffic wave. This paper presents a method and model to simulate the MAGLEV's operation considering continuous demand changes. For this purpose, we employed the discrete event model which is suitable for modeling the behavior of railway passenger transportation, and modeled the system hierarchically using DEVS (Discrete Event System Specification) formalism. In addition, through the implementation and experiment using DEVSim++ simulation environment, we tested the feasibility of the proposed model and it is also verified that our demand-driven simulation technology could be used for the prior review of the train operation plans and strategies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.37
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• pp.31-40
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• 1996

The amount of safety stock is decided from various information such as the forecasted demand, the lead time, the size of the order quantity and the desired service level. There are two cases to consider the problem of setting safety stock when both the demand in a period and the lead time are characterized as random variables: the first case is the parameters of the demand and lead time distributions are known, the second case is they are unknown and must be estimated. The objective of this study is to present the procedure for setting safety stocks in the case the parameters of the demand and lead time distributions are unknown and must be estimated. In this study, a simple exponential smoothing model is used. to generate the estimates of demand in each period and a discrete distribution of the lead time is developed from historical data, and the optimal service level is used which determined to consider both of a backorder and lost sale.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.110-117
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• 2012

The demand for facility used in producing multi-products is changed dynamically for discrete and finite time periods. The excess or the shortage for facility is occurred according to difference of the facility capacity size and demand for facility through given time periods. The shortage facility is met through the outsourcing production. The excess facility cost is considered for the periods that the facility capacity is greater than the demand for the facility, and the outsourcing production cost is considered for the periods that the demand for facility is greater than the facility capacity. This paper addresses to determine the facility capacity size, outsourcing production products and amount that minimizes the sum of the facility capacity cost, the excess facility cost and the outsourcing production cost. The characteristics of the optimal solution are analyzed, and an algorithm applying them is developed. A numerical example is shown to explain the problem.