• Land and Housing Review
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• v.9 no.4
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• pp.10-18
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• 2018

As the volatility increasement of the number of tourist, there was been controversy over supply-demand imbalance in hotel market. The purpose of this study is to analysis on determinants of hotel occupancy rate in Jeju Island. The quantitative method is based on cointegrating regression, using an empirical dataset with hotel from 2000 to 2017. The primary results of research is briefly summarized as follows; First, there are high relationship between total hotel occupancy rate and hotel occupancy of foreign tourist. The volatility of hotel occupancy is caused by foreigner user than local tourists though local tourist high propotion of hotel occupancy in Jeju Island. Second, hotel occupancy of local tourist has not relationship with demand and supply variables. Because some hotel users are not local tourists but local resident, and effects to other variables of hotel consumer trend, accommodation such as Guest house, Airbnb. Third, there are high relationship between foreign hotel occupancy rate and demand-supply variables. These research imply that total management of supply-demand is very important to seek stability of hotel occupancy rate in Jeju Island. Also it can provide a useful solution regarding mismatch problem between supply-demand as well as development the systematic forecasting model for hotel market participants.

• Korean Management Science Review
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• v.1
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• pp.35-45
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• 1984

This paper deals with the case study of the establishment of decision supporting system in shipbuilding industory. Facts or information of shipbuilding, sales, finance, production strategic planning in shipbuilding industry are considered. General transportation model for shipyard production schedule is formulated, and shipbuilding demand forecasting scheme is also introduced. This paper shows the several methods of DSS in shipbuilding industry. But production schedule strategic planning system by OR technique is emphasized. For the realization of DSS in shipbuilding industry, another efforts (data gathering and programming etc.) should be given on the basis of these methods.

• Journal of Korean Society of Transportation
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• v.24 no.4 s.90
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• pp.7-17
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• 2006

The purpose or this paper is to investigate the relationship between highway improvement and Induced Travel Demand(ITD) focusing on the Seoul metropolitan area data. In addition, authors tried to estimate basic unit of demand elasticity focusing on zone and trip purpose which can be applied for the ITD forecasting. The results are based on the 2002 Metropolitan Household Transportation Survey Data, where the demand elasticity (DE) is -0.582 in Seoul, -0.597 in Incheon and -0.559 in Gyounggi province, respectively. This study revealed part of the relationship between highway improvement and ITD for metropolitan region and provided the framework for yielding real estimated values by applying the concept of demand elasticity in terms of the relationship by using regional and long-term data. We expect that the basic unit of demand elasticity focusing on zone and trip purpose can be applied for the ITD forecasting to analyze the whole demand exactly The estimated DE's for age group and day of week can also be used for Proper transportation management and transport Policy making. Some limitations have also been discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.2
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• pp.113-125
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• 2012

The logistic model and the Bass model have diverse names and formulae in diffusion theory. This diversity makes users or readers confused while it also contributes to the flexibility of modeling. The method of handling the integration constant, which is generated in process of deriving the closed form solution of the differential equation for a diffusion model, results in two different 'actual' models. We rename the actual four models and propose the usage of the models with respect to the purpose of model applications. The application purpose would be the explanation of historical diffusion pattern or the forecasting of future demand. Empirical validation with 86 historical diffusion data shows that misuse of the models can draw improper conclusions for the explanation of historical diffusion pattern.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.139-145
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• 1996

A forecast on the past output data sets of small bar steels is very important information to make a decision on the future production quantities. In many cases, however, it has been mainly determined by experience (or rule of thumb). In this paper, past basic data sets of each small bar steels are statistically analyzed by some graphical and statistical forecasting methods. This work is mainly done by SAS. Among various quantitative forecasting methods in SAS, STEPAR forecasting method was best performed to the above data sets. By the method, the future production quantities of each small bar steels are forecasted. As a result of this statistical analysis, 95% confidence intervals for future forecast quantities are very wide. To improve this problem, a suitable systematic database system, integrated management system of demand-production-inventory and integrated computer system should be required.

• Environmental and Resource Economics Review
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• v.27 no.2
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• pp.287-313
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• 2018

This paper measures and analyzes cooling and heating demand in Korean electricity demand using time-varying temperature response functions and cooling and heating temperature effects. We fit the model to Korean data for residential and commercial sector over 1999:01~2016:12 and the estimation results show that the growth rate of heating demand is much higher than that of base and cooling demand, and especially the growth rate of heating demand in commercial sector is much higher. And we define the temperature-normalized demand conditioning that monthly temperatures are assumed as average monthly temperatures. The growth rate of heating demand in the estimated temperature-normalized demand is higher than that in the real demand. Our results are expected to be a base data for Winter Demand Management and short-term electricity demand forecasting.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.261-273
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• 2022

This study aims to predict the land use demand of Seongnam-city using system dynamics and to simulate the effect of changes in family structure and land use density adjustment policy on land use demand. This study attempted to construct causal loop diagrams and an analysis model. The changes in land use demand over time were predicted through simulation results. As a result of the analysis, as of 2035, an additional supply of 2.08 km² for residential land and 1.36 km² for commercial land is required. Additionally, the current supply area of industrial land can meet the demand. Three policy experiments were conducted by changing the variable values in the basic model. In the first policy experiment, it was found that when the number of household members decreased sharply compared to the basic model, up to 7.99 km² of additional residential land were required. In the second policy experiment, if the apartment floor area ratio was raised from 200% to 300%, it was possible to meet the demand for residential land with the current supply area of Seongnam-city. In the third policy experiment, it was found that even if the average number of floors in the commercial area was raised from four to five and the building-to-land ratio in the commercial area was raised from 80% to 85%, the demand for commercial land exceeded the supply area of the commercial area in Seongnam-city. This study is meaningful in that it proposes a new analytical model for land use demand prediction using system dynamics, and empirically analyzes the model by applying the actual urban planning status and statistics of Seongnam-city.

• CRM연구
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• v.1 no.1
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• pp.49-69
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• 2006

Previous studies, Technology Acceptance Model (TAM) and Post Acceptance Model (PAM) have a little limitation in time series analysis. To solve this limitation, we used system dynamics as research methodology and designed simulation model based on TAM and PAM. Moreover, we designed new simulation model which can analyize time series data in customers' demand change from initial acceptance to post acceptance. This study targeted domestic mobile phone market. The simulation results showed that diffusion graph was similar to real data. That means we validated our simulation model. Since the simulation model offers the graph of customer's demand change by time, so it can be useful as a leaning tool. Therefore, we think this study helps IT companies use the model for forecasting of market demand.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.105-118
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• 2008

There are many debates on the topic of the relationship between oil prices and economic growth. Through the repeated processes of conformations and contractions on the subject, two main issues are developed; one is how to define and drive oil shocks from oil prices, and the other is how to specify an econometric model to reflect the asymmetric relations between oil prices and output growth. The study, thus, introduces the unobserved component model to pick up the oil shocks and a first-order Markov switching model to reflect the asymmetric features. We finally employ unique oil shock variables from the stochastic trend components of oil prices and adapt four lags of the mean growth Markov Switching model. The results indicate that oil shocks exert more impact to recessionary state than expansionary state and the supply-side oil shocks are more persistent and significant than the demand-side shocks.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.