• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.159-168
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• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• The Journal of Korean Medicine
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• v.28 no.1 s.69
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• pp.42-50
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• 2007

Objectives . This study was to assess the Korean Traditional Medicine forecast subjects that had been expected to be accomplished by 2005. The result will help the Korean medical society plan far policies and studies on Korean Traditional Medicine. Methods : Assessed targets were 64 subjects (expected to be studied until 2005) of the total 93 subjects from the 'Mid- to Long-Term Forecast and Plan Study for Korean Traditional Medicine'. The subjects were classified into two types : political subjects and research and development (R&D) subjects. These were determined by the quantity and contents of related political reports, political research projects, thesis, patent, placing products on sale, etc. Results :1) 5 items of a total 12 political subjects were accomplished or partially accomplished (41.7%), and 9 items of a total 46 R&D subjects were accomplished or partially accomplished (9.5%). 2) While the accomplishment percentage (accomplished or partial accomplished) in literature arrangement and D/B construction field was 100%, it was under 10% in product or system development field. Thus, it seems that practical subjects were less accomplished than academic subjects. 3) On 8 subjects of 'Forecast Research on Future of Oriental Medicine' which had been performed in Japan, the Korean expected dates when the subjects would be realized were earlier than the Japanese ones, but no subjects were realized. Conclusion · Political and academic subjects weir accomplished more than R&D and practical subjects.

• Korean System Dynamics Review
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• v.5 no.2
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• pp.67-88
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• 2004

This paper attempts to explore the generic pitfalls of the traditional number-crunching methods adopted thus far for the forecast of newly emerging market trends, and present an alternative by introducingsystems thinking to the portable Internet service market as an example, followed by its rationale as a new tool for forecasting and some reasoning about why traditional methods are no longer appropriate. Most adoption models in general to forecast market trends have several limitations due to theirbasic assumptions and prospective. First, they fail to capture dynamic interactions among the factors involved over time, with implicit assumptions of 'unilateral causality' in that each factor contributes as a cause to the effect, i.e., causality runs one way; each factor acts independently the weighting factor of each is fixed, etc. Second, the number-crunching models have no way of taking into account the impact of delayed feedback often caused by introducing new policies and legislative changes on the whole system under investigation. Third, there is not a way to reflect the effect of competition by players.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.259-260
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• 2023

The rapid urbanization and modernization observed in countries like Myanmar have led to significant concerns regarding traffic congestion, especially in urban areas. This study focuses on the analysis and revitalization of urban transport in selected areas of Myanmar. The core of urban transportation planning lies in travel forecasting, which employs models to predict future traffic patterns and guide decisions related to road capacity, transit services, and land use policies. Travel demand modeling involves a series of mathematical models that simulate traveler behavior and decision-making within a transportation system, including highways, transit options, and policies. The paper offers an overview of the traditional four-step transportation modeling system, utilizing a simplified transport network in the context of Mandalay City, Myanmar.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.286-293
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• 2020

Since Dr. Lipke announces earned schedule management(ESM) in 2002, it has been used in project management to make up for the insufficient schedule management function of earned value management technique. However, it is difficult to accurately forecast the schedule of delayed defense research and development(R&D) projects with the ESM technique. Therefore, this paper proposes a new schedule forecasting method considering the progress of delayed work in ESM technique. This concept can also be adopted to the traditional project progress management (PPM) technique. We verify the effectiveness of the proposed concept through several defense R&D projects and prove that it is possible to supplement the schedule forecasting of the ESM and PPM technique.

• IE interfaces
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• v.13 no.4
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• pp.646-657
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• 2000

We developed an efficient small sized weather condition forecasting system (WFS). A cheap NT-server was utilized for handling a large amount of data, while traditional WFS has conventionally relied on Unix based workstation server. The proposed WFS contains automatic weather observing system (AWS). AWS was designed for collecting weather conditions automatically, and it was linked to WFS in order to provide various weather condition information. The existing two phase scheme and chain code algorithm were used for transforming AWS's data into WFS's data. The WFS's data were mapped into geometric information system using various display techniques. Finally the transformed WFS's data was also converted into JPG (Joint Photographic Group) data type, and the final JPG data could be accessible by others though Internet. The developed system was implemented using WWW environment and has provided weather condition forecasting information. Real case is given to show the presented integrated WFS with detail information.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.4
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• pp.567-574
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• 2019

Traditional project progress method(PPM) has been used for Korean defense research and development project management for the last 20 years. However, it is difficult to intuitively understand the performance in terms of the project schedule, because the PPM does not provide the function of managing and forecasting project schedule. Therefore, this paper proposes new schedule managing and forecasting function for the PPM using earned schedule management concept. We verify the effectiveness of the proposed functions through several defense projects and prove that it is possible to reinforce the schedule management function of the PPM.

• Korea journal of population studies
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• v.33 no.1
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• pp.161-189
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• 2010

Dealing with uncertainty has been a critical issue in demographic and population forecasting since 1980. This study reviews methodological developments in demographic and population forecasting over the last several decades. First, this study reviews the important issue of the uncertainty surrounding demographic forecasts. Several limitations of the traditional scenario approach to dealing with uncertainty are also discussed. Second, in forecasting demographic processes such as mortality, fertility, and migration, three approaches of stochastic forecasting are identified and discussed: expert judgment, statistical modeling, and analysis of historical forecast errors. Finally, this study discusses the current issues and directions for future research in stochastic demographic forecasting.