• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.185-190
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• 1993

In this paper, we investigate the effect of production uncertainty - especially demand fluctuation and activity time variation, to production control policies. First, we examine three famous production control policies, namely, MRP, JIT, OPT, from shop floor control perspective and analyze the difference among them. Based on these, simulation studies are performed to draw out the effects of demand fluctuation which are classified into demand lumpiness and demand irregularity, and, the effects of activity time variation which are classified into standard time variation and non-standard time variation. Experimental investigation shows that, in terms of demand fluctuations, MRP is affected by demand lumpiness, but JIT by demand irregularity. And we also see that both MRP and JIT are influenced by standard time variation with respect to activity time variations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.17-22
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• 2013

Fuzzy linear regression method has been used for short-term load forecasting of the special day in the previous researches. However, considerable load forecasting errors would be occurring if a special day is located on Saturday or Monday. In this paper, a new load forecasting method for the consecutive holidays is proposed with the consideration of the power demand variation rate. In the proposed method, a exponential smoothing model reflecting temperature is used to short-term load forecasting for Sunday during the consecutive holidays and then the loads of the special day during the consecutive holidays is calculated using the hourly power demand variation rate between the previous similar consecutive holidays. The proposed method is tested with 10 cases of the consecutive holidays from 2009 to 2012. Test results show that the average accuracy of the proposed method is improved about 2.96% by comparison with the fuzzy linear regression method.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.2
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• pp.187-196
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• 2006

Forecasting the long-term water demand is important in the plan of water supply system because the location and capacity of water facilities are decided according to it. To forecast the long-term water demand, the existing method based on lpcd and population has been usually used. But, these days the trend among the variation of water demand has been disappeared, so expressing other variation of it is needed to forecast correct water demand. To accomplish it, we introduced the System Dynamics method to consider total connections of water demand factor. Firstly, the factors connected with water demand were divided into three sectors(water demand, industry, and population sectors), and the connections of factors were set with multiple regression model. And it was compared to existing method. The results are as followings. The correlation efficients are 0.330 in existing model and 0.960 in SD model and MAE are 3.96% in existing model and 1.68% in SD model. So, it is proved that SD model is superior to the existing model. To forecast the long-term water demand, scenarios were made with variations of employment condition, economic condition and consumer price indexes and forecasted water demands in 2012. After all scenarios were performed, the results showed that it was not needed to increase the water supply ability in Seoul.

• Journal of Information Technology Applications and Management
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• v.30 no.3
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• pp.1-13
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• 2023

Demand forecasting is a crucial task for an online retail where has to manage daily fresh foods effectively. Failing in forecasting results loss of profitability because of incompetent inventory management. This study investigated the optimal performance of different forecasting models for a very short shelf-life product. Demand data of 13 perishable items with aging of 210 days were used for analysis. Our comparison results of four methods: Trivial Identity, Seasonal Naïve, Feed-Forward and Autoregressive Recurrent Neural Networks (DeepAR) reveals that DeepAR outperforms with the lowest MAPE. This study also suggests the managerial implications by employing coefficient of variation (CV) as demand variation indicators. Three classes: Low, Medium and High variation are introduced for classify 13 products into groups. Our analysis found that DeepAR is suitable for medium and high variations, while the low group can use any methods. With this approach, the case can gain benefit of better fill-rate performance.

• Korean Management Science Review
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• v.19 no.1
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• pp.1-12
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• 2002

Performances of common zone with various sizes are examined for possible demand rate variations for an AS/RS. Common zone is a middle area located between the 1st class and the 2nd class to be used by the 1st class Items if the assigned racks are not enough. This area is designed to resolve the rack shortage problem associated with a particular class. In the previous researches, effect of demand rate variation is Ignored since flxed demand rate is assumed. It is found that rack shortage rate is decreased up to 67% of common zone size. However, no difference is found at above 70%. Waiting time tends to be longer with Increased common zone while travel time and throughput are not affected at all with the common zone sloe.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.2
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• pp.86-96
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• 2010

The paper describes development and evaluation of a simple method for determining gradient of modified linear setpoint variation to reduce peak electrical cooling demand in buildings using building precooling and setpoint adjustment. The method is an approximated approach for minimizing electrical cooling demand during occupied period in buildings and involves modified linear adjustment of cooling setpoint temperature between $26^{\circ}C$ and $28^{\circ}C$. The gradient of linear variation or final time of linear increase is determined based on the cooling load shape in conventional cooling control having a constant setpoint temperature. The potential to reduce peak cooling demand using the simple method was evaluated through building simulation for a calibrated office building model considering four different weather conditions. The simple method showed about 30% and 20% in terms of reducing peak cooling demand and chiller power consumption, respectively, compared to the conventional control.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.403-420
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• 1997

In this paper, we examine the effect of production uncertainty to production control policies. First, we examine two famous production control policies, namely, MRP and JIT from the view point of shop floor control perspective, and analyze the differences between them due to demand fluctuations and activity time variations. Second, we conduct simulation studies on MRP and JIT to draw out the effects of demand fluctuations and activity time variations. Demand fluctuations are further classified into demand lumpiness and demand irregularity. And, activity time variations are further classified into stationary time variations and non-stationary time variations. Experimental results show that, in terms of demand fluctuations, MRP is affected by demand lumpiness, but JIT by demand irregularity. And we also see that both MRP and JIT are influenced by stationary time variation with respect to activity time variations.

• Environmental and Resource Economics Review
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• v.19 no.2
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• pp.283-301
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• 2010

This study estimated the paper demand and supply using VAR model. And the variance decomposition and impulse response were analyzed using the model. In the model of paper demand, the own price change accounts for about seventeen percent of variation in the demand, and the gross domestic product change accounts for about twenty eight percent of variation in the demand. And the impacts of a shock to the own price and gross domestic product are significant for about six months on the demand for paper. In the model of paper supply, the own price change accounts for about twenty nine percent of variation in the supply, and the pulp price change accounts for about twelve percent of variation in the supply. And the impacts of a shock to the own price and pulp price are significant for about six months on the supply of paper.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.431-434
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• 2006

Due to rapid technology shifts and demand uncertainty, there is a high risk that inventoried products will become obsolete. Consequently companies have to decide capacities considering product life cycle and demand variation. In this paper, 1 will analyze previous research, and then provide taxonomy of them and propose further research directions.

• ETRI Journal
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• v.44 no.4
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• pp.624-640
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• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.