• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(II)
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• pp.3-7
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• 2003

It is very difficult to find optimal train operation plan when analyzing the economic investment using traditional railroad travel demand estimation method. Train operation plan depends on travel demand and vice versa. To solve this problem, this study suggests a demand estimation method to address an optimal train operation scheme with the modal spilt using initial train operation plan and trip assignment.

• 산업경영시스템학회지
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• 제43권4호
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

• 조명전기설비학회논문지
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• 제26권2호
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• pp.18-26
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• 2012

Though electricity consumption amount in industry has been increased gradually, corresponding power supply show symptoms of marginal point. Importance of demand-side management from large-industries has also been raised. This paper deals with induction motor, which is one of representative examples of heavy electricity consumption utilities, to analyze potential technical capability, economic feasibility from consumers' viewpoint and demand-side management feasibility from nation-wide perspective. Nation-wide economic feasibility analysis was done through California test, which has been used as demand-side management evaluation model. This paper also describes limitation of existing high efficiency induction motor in terms of contribution to demand-side management and utilizes premium motor to calculate demand-side management contribution level and economic feasibility evaluation. Likewise, this paper emphasizes the efficiency improvement of induction motor and analyzes how much premium motor related technologies can contribute to demand-side management.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1993년도 추계학술대회발표논문집; 서강대학교, 서울; 25 Sep. 1993
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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.

• 전기학회논문지
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• 제66권1호
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• pp.23-26
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• 2017

Social Welfare is useful concept for evaluating the effectiveness of an economic policy in micro economics. This paper focuses on Social Welfare(SW) of electricity market incorporating demand response(DR). Competition between DR and generation company is modeled as a simple bid function. DR function can be considered as an negative generation(called Negawatt) and as an element of modified demand function. These two approaches result in the same demand reduction, generation power, and the market price. However, SW in the modified demand function approach is not identical to SW in the Negawatt approach. It makes the numerical index of DR effectiveness less persuasive. This paper proposes modified definition of SW in the demand function approach. The proposed definition of SW leads the DR effectiveness index to be identical to that in the Negawatt approach.

• Safety and Health at Work
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• 제10권1호
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• pp.61-66
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• 2019

Background: Emergency workers such as firefighters are cataloged within the most demanding and injurious professions globally. Considering the health and safety implications in firefighting, a lot of research needs to be conducted to examine how firefighters' task characteristics and their work demand influence the development of work-related musculoskeletal disorders (WMSDs). This study therefore examines how the task characteristics of firefighters moderates the relationship between their work demand and the development of WMSDs. Methods: Convenient sampling was employed to select 320 firefighters in the Greater Accra region of Ghana. Data were analyzed using descriptive statistics, correlation, and regression. Results: The findings revealed that work demand and task characteristics have significant positive effects on WMSDs. Again, the task characteristics of firefighters moderates the relationship between their work demand and WMSDs. Conclusion: Since the study shows that task characteristics influences the relationship between work demand and WMSDs, it is vital for managers to constantly modify the nature of tasks performed by and work demand of emergency workers to minimize the development of WMSDs and other industrial health complications.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• 대한한의학회지
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• 제19권1호
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• pp.299-326
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• 1998

1. Comparison of demand and supply A. Assumption of estimation of demand and supply we will briefly assumptions used for presumption once more before comparing the result of estimation of demand and supply examined previously 1) supply - The average applying rate for state. examination of graduate: ${\alpha}$=1.03109 - The ratio of successful applicants of state examinations: ${\beta}$=0.97091 - Mortality classified by age : presumed data of the Bureau of statistics - Emigrating rate: 0 % - Time of retire: unconsidered - An army doctor number: unconsidered and regard number of employed oriental medicine doctor. - Standard of 1995 : The number of survival oriental medicine doctor is 8195. the number of employed oriental medicine doctor is 7419. 2) demand - derivated demand method Daily the average amount of medical treatment: according to medical insurance federation data. there is 16 or 6 non allowance patient, we consider amount of medical treatment as 22 persons in practical because 21.94 persons (founded practical examination) are converted to allowance in comming demand. Daily the proper amount of medical treatment: 7 hours form -35 persons 5 hours 30 minutes form -28 persons. Yearly medical treatment days: 229 days. 255 days. 269 days . Increasing rate of visiting hospital days: -1996 year. 1997 year. 1998 year- . Rate of applying insurance: yearly average 71.51% (among the investigated patient) B. Comparison of total sum result 1) supply (provision) Table Ⅳ-1 below shows the estimation of the oriental medicine doctor in the future.