• 대한안전경영과학회지
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• 제18권4호
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• pp.73-80
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• 2016

This study highlights the main effect of job demand, work shift, work environment and stressors on the railway traffic controller's health, and the moderating effect of work0life balance. The result of empirical analysis based on questionnaires received from 328 traffic controllers working at 10 railway operating companies indicates that job demand, work shift, work environment and stressors have significant effect on their health, among which stressors is a major factor. In the respect of moderating effect, WLB showed no significance except for job demand. This result implies that controller's health can not be enhanced through their individual family or leisure life. Therefore, effective countermeasures and policy to mitigate their health problems and heal their symptoms are urgent.

• 산업경영시스템학회지
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• 제41권1호
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• 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.

• 한국의류학회지
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• 제32권3호
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• pp.474-485
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• 2008

The purpose of this study is to examine the determinants and elements to enhance Korean international competitiveness, employing Porter's(1998) Diamond Model. Half of the 500 leading apparel exporters that were members of the Korea Apparel Industry Association in 2003 were selected as the target of this research. From May to June of 2003, survey questionnaires were sent to executives of these 250 companies in person or by telephone, e-mail, or fax. Seventy questionnaires were used for the final data anlysis. The items used were Reliability, Categorical Regression, and Frequency, using SPSS 11.5. The results were as follows: First, as a result of analyzing the influence of international competitiveness in Korean apparel industry, the firm's strategy, structure, and rivalry was the most influential factor. Others were related and supporting industries, government, chance, demand conditions, and factor conditions. Also, the elements that affect Korean international competitiveness were listed as the level of price competition in foreign markets, the level of labor cost, export marketing capacity, and exchange fluctuation. The most important element to improve the international competitiveness of the Korean apparel industry was a demand growth rate of the overseas markets(Demand Conditions), followed by the level of the labor costs(Factor Conditions), the capability of internationalization(Firm Strategy, Structure, and Rivalry), the change of currency(Chance), the quality and management of products(Demand Conditions), the capability of planning products(Firm Strategy, Structure, and Rivalry), free trade from 2005(Chance), and global sourcing strategy(Firm Strategy, Structure, and Rivalry). Korea's main rival country in apparel related and supporting industry factors is China. However, Korea has a higher level of technology development, quality, and price level than China.

• 안보군사학연구
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• 통권5호
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• pp.241-280
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• 2007

This study examines the relationship of the industry-specific factors that effect innovation of manufacturing technology and the market share within the defense industry. Since the establishment of the basic defense industry framework in 1973, there were numerous interactions of the industry-specific factors of the defense industry structure with the technological innovation and market organization of the defense industry. During last three decades, the domestic defense industry has achieved the considerable level but the framework of the basic system has not developed much in areas of the military science and the defense manufacturing technology. Industry-specific factors were formed in the process and appeared in a variety of behavioral characteristics as subsystems. Currently, there IS a growing trend where the management of defense industry is gradually deteriorating due to limitation of the domestic industry-specific factor (e.g. defense technologies, amount of demand, etc.). If there is a prominent imbalance of the industry-specific factors. it can trigger the potential problem of conflict, lack of cooperation and control, slowing the growth of the manufacturing technology thereby diminishing the market and deteriorating the defense supply/demand relationship. In a research conducted by Joe S. Bain, Bain analyzed the relationship of the traditional industrial organization where industry-specific factor(S) not only impacts the conductor(C). And, conductor(C) influences the shaping of the performance(P) of relationship of the traditional industrial organization. Consequently, the researcher has identified the demand monopoly, barriers to entry, and market competition with comparison of defense industry issues. These defense issues were three industry-specific factors identified, which are 1) The demand monopoly and The entry barriers to new market competition, 2) the industrial technical factor to a production technical competitiveness and a market sharing competitiveness, 3) the probability factor to revolution for military affairs(RMA) and a R&D production. According to baseline with these factors, the following research model is established from the special companies group(Group A), the systematization companies group(Group B), and the general companies group(Group 0. The hypothesis is that if there are more industry-specific factors, then there will be more relationships of defense industry relation statutes. This research is an empirical study on the relationship that the industry specific factors effects the innovation of manufacturing technology and the shaping of the market in the defense industry. Moreover, the existing models to evaluate the industry specific factors of the defense industry IS much to be desired with the controlled statistical analysis of the result. It is vital to study on current situation with suggesting alternative strategy to the efficient strategy. The descriptive analysis approach analysis is conducted with SPSSWIN to conduct reliability test, factor analysis, correlation analysis, cross-tabulation analysis, one-way ANOVA, and multiple regression analysis. However, there were some limitations of the survey such as the rigidity of concept about the technical factors and various market management factors. The wishes is that the decision-maker could be utilized these defence industrial factors to formulate efficient defence policy and strategy in the future.

• Earthquakes and Structures
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• 제12권4호
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• pp.397-407
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• 2017

To estimate the structural seismic demand, some methods are based on an equivalent linear system such as the Capacity Spectrum Method, the N2 method and the Equivalent Linearization method. Another category, widely investigated, is based on displacement correction such as the Displacement Coefficient Method and the Coefficient Method. Its basic concept consists in converting the elastic linear displacement of an equivalent Single Degree of Freedom system (SDOF) into a corresponding inelastic displacement. It relies on adequate modifying or reduction coefficient such as the inelastic deformation ratio which is usually developed for systems with known ductility factors ($C_{\mu}$) and ($C_R$) for known yield-strength reduction factor. The present paper proposes a rational approach which estimates this inelastic deformation ratio for SDOF bilinear systems by rigorous nonlinear analysis. It proposes a new inelastic deformation ratio which unifies and combines both $C_{\mu}$ and $C_R$ effects. It is defined by the ratio between the inelastic and elastic maximum lateral displacement demands. Three options are investigated in order to express the inelastic response spectra in terms of: ductility demand, yield strength reduction factor, and inelastic deformation ratio which depends on the period, the post-to-preyield stiffness ratio, the yield strength and the peak ground acceleration. This new inelastic deformation ratio ($C_{\eta}$) is describes the response spectra and is related to the capacity curve (pushover curve): normalized yield strength coefficient (${\eta}$), post-to-preyield stiffness ratio (${\alpha}$), natural period (T), peak ductility factor (${\mu}$), and the yield strength reduction factor ($R_y$). For illustrative purposes, instantaneous ductility demand and yield strength reduction factor for a SDOF system subject to various recorded motions (El-Centro 1940 (N/S), Boumerdes: Algeria 2003). The method accuracy is investigated and compared to classical formulations, for various hysteretic models and values of the normalized yield strength coefficient (${\eta}$), post-to-preyield stiffness ratio (${\alpha}$), and natural period (T). Though the ductility demand and yield strength reduction factor differ greatly for some given T and ${\eta}$ ranges, they remain take close when ${\eta}>1$, whereas they are equal to 1 for periods $T{\geq}1s$.

• 교통기술과정책
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• 제7권3호
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• pp.31-35
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• 2010

• 정보과학회지
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• 제23권5호
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• pp.70-75
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• 2005

• 조명전기설비학회논문지
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• 제22권2호
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• pp.19-25
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• 2008

본 논문에서는 시장전환 메커니즘을 활용하여 심야전력제도의 개선 방안을 새롭게 제안하였다. 심야전력 창출을 위해 도입된 심야전력제도는 현재 국가적으로 경제적 손실의 가중과 에너지 시장의 왜곡을 확대하는 결과를 가져오고 있다. 이에 따라 본 논문에서는 심야전력제도와 관련된 모든 이해당사자의 경제적 비용편익 분석을 통해 국가적으로 가장 비용 효과적이며 효율적인 시장전환 방안을 제시하였으며, 이를 위해 요금제도의 정상화를 위해 수년간 요금인상 후 심야전력제도를 폐지하는 방안을 제시하였으며, 심야전력 소비자와 사업자의 보호를 위해 복수의 수명방안에 대한 보상방안을 분석하였다.

• Earthquakes and Structures
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• 제19권5호
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• pp.379-393
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• 2020

The purpose of this study is to first evaluate the seismic behavior of ageing arch bridges by using the Intensity Measure - based demand and DCFD format, which is referred to as the fragility-hazard format. Then, an investigation is performed for their seismic vulnerability. Analytical models are created for bridges concerning different features and these models are subjected to Incremental Dynamic Analysis (IDA) analysis using a set of 22 earthquake records. The hazard curve and results of IDA analysis are employed to evaluate the return period of exceeding the limit states in the IM-based probabilistic performance-based context. Subsequently, the fragility-hazard format is used to assess factored demand, factored capacity, and the ratio of the factored demand to the factored capacity of the models with respect to different performance objectives. Finally, the vulnerability curves are obtained for the investigated bridges in terms of the loss ratio. The results revealed that decreasing the span length of the unreinforced arch bridges leads to the increase in the return period of exceeding various limit states and factored capacity and decrease in the displacement demand, the probability of failure, the factored demand, as well as the factored demand to factored capacity ratios, loss ratio, and seismic vulnerability. Finally, it is derived that the probability of the need for rehabilitation increases by an increase in the span length of the models.