• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.343-348
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• 2002

We are concerned with a long-term replenishment contract for the ARIMA demand process in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy but allows us to contract future replenishments at a time with a price discount. Due to the larger forecast error of future demand, the buyer should keep a higher level of safety stock to provide the same level of service as the usual (s, Q) policy. However, the buyer can reduce his purchase cost by ordering a larger quantity at a discounted price. Hence, there exists a trade-off between the price discount and the inventory holding cost. For the ARIMA demand process, we present a model for the contract and an algorithm to find the number of the future replenishments. Numerical experiments show that the proposed algorithm is efficient and accurate.

• Korean Management Science Review
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• v.27 no.2
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• pp.31-44
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• 2010

It is very important to forecast demand and determine the optimal procurement quantities of spare parts. The Army has been forecasting demand not with actual usage of spare parts but with request quantities. However, the Army could not purchase all of forecasted demand quantities due to budget limit. Thus, the procurement quantities depend on the item managers' intuition and their meetings. The system currently used contains many problems. This study suggests a new determination procedure; 1) forecasting demand method based on actual usage, 2) determining procurement method through LP model with budge and other constraints. The newly determined quantities of spare parts is verified in the simulation model, that represents the real operational and maintenance situation to measure the operational availability. The result shows that the new forecasting method with actual usage improves the operational availability. Also, the procurement determination with LP improves the operational availability as well.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.104.1-104
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• 2001

This paper presents a technique to development load control and management system in order to limits a maximum load demand and saves electric energy consumption. The computer programming proper load forecasting algorithm associated with programmable logic control and digital power meter through inform of multidrop network RS 485 over the twisted pair, over all are contained in this system. The digital power meter can measure a load data such as V, I, pf, P, Q, kWh, kVarh, etc., to be collected in statistics data convey to data base system on microcomputer and then analyzed a moving linear regression of load to forecast load demand Eventually, the result by forecasting are used for compost of load management and shedding for demand monitoring, Cycling on/off load control, Timer control, and Direct control. In this case can effectively reduce the electric energy consumption cost for 10% ...

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.4
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• pp.157-162
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• 2009

This study's purpose is to forecast the market demand of UVS (u-Transportation Vehicle Sensor) OBU (On-board Unit) of the ubiquitous Transportation. Bass model, Logistic model, and Gompertz model were used for the forecasting market demand. Firstly, this research focused on the market size for the u-T OBU. All three models were used for the market size prediction and the average values were used. The Bass model were calibrated and the market demand for the UVS OBU of the u-Transportation system were estimated using this model.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.235-240
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• 2006

The existing inventory managements bear a relation to forecasting or assumptions. So these methods become more complicated and more expensive systems as time goes. This paper developed a practical inventory system which is called DCC(demand control chart). DCC does not 'forecast' but 'control' the trend of demand without assumptions. According to the trend of sales, DCC adjusts an order quantity considering the capacity of shelf in a store. Specially, DCC is a useful method under FRID system. Besides, this paper introduces EPFR(Every Period Full Replenishment) policy for reducing stocks.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.216-228
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• 2011

A logistics system involving a supplier who produces and delivers a single product and a buyer who receives and sells the product to the final customers is analyzed. In this system, the supplier and the buyer establish a contract which specifies that the supplier will deliver necessary amount of the product to raise inventory up to a specified position at the beginning of each period. A new periodic order-up-to-level inventory control policy specifically designed for nonstationary end customer's demand is proposed for the system. Simulations are used to test the efficiency of the proposed policy. An analysis of the test results reveals that the proposed policy performs much better than does the existing order-up-to-level policy, especially when the demand is nonstationary.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.4
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• pp.377-389
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• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• Journal of Hydrogen and New Energy
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• v.33 no.5
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.45-55
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• 2020

Recently, as the awareness of fine dust has increased in Korea, various countermeasures have been suggested. This study examines the current status of fine dust free zones at home and abroad in order to analyze changes in guest space according to the occurrence of fine dust and to find activity patterns. I would like to predict and find implications. The purpose of this study is to forecast demand centering on domestic and foreign countermeasures for dust and domestic industry. In order to secure competitiveness for the smart city in the era of the 4th Industrial Revolution, the research is aimed at proposing a strategic plan to cope with the fine dust that is a threat to urban space. The research method is described in the following order.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.127-135
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• 2009

It is necessary to forecast the amount of the maximum electricity demand for stabilizing the flow of electricity. The time series data was collected from the Korea Energy Research between January 2000 and December 2006. The data showed that they had a strong linear trend and seasonal change. Winters seasonal model, ARMA model were used to examine it. Root mean squared prediction error and mean absolute percentage prediction error were a criteria to select the best model. In addition, a nonstationary generalized extreme value distribution with explanatory variables was fitted to forecast the maximum electricity.