• Journal of the Economic Geographical Society of Korea
• /
• v.15 no.2
• /
• pp.304-313
• /
• 2012

Uncertainty increases as a result of environment change and change of individual decision-making in extreme weather. This study consider individual decision-making which has been not covered until now. The purpose of this study is making Agent-Based Model to predict it more accurate that how much change travel demand in heavy rain and heavy snow. Through this model, it can be utilized to forecast travel demand, changes in travel behavior and traffic patterns. It will be also possible to predict discomfort index and risk of accidents.

• Journal of Korea Multimedia Society
• /
• v.19 no.8
• /
• pp.1516-1529
• /
• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Journal of Advanced Navigation Technology
• /
• v.28 no.4
• /
• pp.450-458
• /
• 2024

Urban air mobility (UAM) shows great promise for commercialization, yet the technical foundations for ensuring safety in large-scale operations remain a challenge. The purpose of this paper was to analyze current air traffic conflict management techniques in order to develop strategies for implementing strategic conflict management within UAM traffic management systems. We found that UAM conflict management can benefit from demand and capacity balancing techniques, and a system-wide approach is essential. Specifically, the use of data sharing and probabilistic methods is essential for addressing flight time uncertainty and large-scale UAM operations effectively.

• The Journal of Bigdata
• /
• v.9 no.1
• /
• pp.31-47
• /
• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Journal of Korean Society of Transportation
• /
• v.35 no.4
• /
• pp.360-373
• /
• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.

• Korean Chemical Engineering Research
• /
• v.58 no.3
• /
• pp.396-407
• /
• 2020

As fossil fuels are depleted worldwide, alternative resources is required to replace fossil fuels, and biofuels are in the spotlight as alternative resources. Biofuels are produced from biomass, which is a renewable resource to produce biofuels or bio-chemicals. Especially, in order to substitute fossil fuels, the research focusing the biofuel (biodiesel) production based on CO₂ and biomass achieves more attention recently. To produce biomass-based biodiesel, the development of a supply chain network is required considering the amounts of feedstocks (ex, CO₂ and water) required producing biodiesel, potential locations and capacities of bio-refineries, and transportations of biodiesel produced at biorefineries to demand cities. Although many studies of the biomass-based biodiesel supply chain network are performed, there are few types of research handled the uncertainty in CO₂ supply which influences the optimal strategies of microalgae-based biodiesel production. Because CO₂, which is used in the production of microalgae-based biodiesel as one of important resources, is captured from the off-gases emitted in power plants, the uncertainty in CO₂ supply from power plants has big impacts on the optimal configuration of the biodiesel supply chain network. Therefore, in this study, to handle those issues, we develop the two-stage stochastic model to determine the optimal strategies of the biodiesel supply chain network considering the uncertainty in CO₂ supply. The goal of the proposed model is to minimize the expected total cost of the biodiesel supply chain network considering the uncertain CO₂ supply as well as satisfy diesel demands at each city. This model conducted a case study satisfying 10% diesel demand in the Republic of Korea. The overall cost of the stochastic model (US$ 12.9/gallon·y) is slightly higher (23%) than that of the deterministic model (US$ 10.5/gallon·y). Fluctuations in CO₂ supply (stochastic model) had a significant impact on the optimal strategies of the biodiesel supply network.

• Journal of the Korean Society for Railway
• /
• v.20 no.1
• /
• pp.156-163
• /
• 2017

In the major operation sections of the urban railway, there has been habitual delay, and delay propagation; another problem is the increase of crowds and of inconvenience to passengers. The urban railway has different characteristics from rural railways, such as uncertainty of demand and irregularity of train operation. In urban railways, recently, operators manage quality indicators of service using operation results, such as the delay of train operation and the congestion of trains. However, because the urban railway has characteristics in which demand, passenger behavior, and train operation mutually affect each other, it is difficult to express the quality of service that passengers actually feel. In this paper, we suggest a quality indicator of service from the viewpoint of passengers, and present a demand responsive multi-train simulation method to predict dynamic dwell time and train operation status; we also use simulation results to consider changes in the quality indicator of service.

• Journal of Korean Society of Steel Construction
• /
• v.18 no.4
• /
• pp.481-490
• /
• 2006

The actual performance of a building during an earthquake depends on many factors. The prediction of the seismic performance of a new or existing structure is complex, due not only to the large number of factors that need to be considered and the complexity of the seismic response, but also due to the large inherent uncertainties and randomness associated with making these predictions. A central issue of this research is the proper treatment and incorporation of these uncertainties and randomness in the evaluation of structural capacity and response has been adopted in the seismic performance evaluation of steel tall buildings to account for the uncertainties and randomness in seismic demand and capacities in a consistent manner. The basic framework for reliability-based seismic performance evaluation and the key factors for statistical studies were summarized. A total of 36 target structures that represent typical tall steel buildings based on national building code (KBC-2005) were designed for the statistical studies of demand factor s and capacity factors. The incremental dynamic analysis (IDA) approach was examined through the simple steel moment frame building in determination of global drift capacity.

• Journal of the Korean Society of Costume
• /
• v.34
• /
• pp.135-150
• /
• 1997

The purpose of this study was to inves-tigate the economic performance of the textile industry for the Korean traditional clothes. The content of this paper had two pars; The first part was for the macroeconomic aspects such as location production employments and the produc-tion facilities of the textile industries. The second part was for the microeconomic aspects such as business type branding method fabric type R&D efforts sourc-ing and the distributional channel The major results were as follows: 1.) Most textile firms for the korean traditional clothes were located in Gongju for man-made fibers and in Jinju for silk fabrics. 2) The size of the textile industry in terms of the number of business produc-tion amount the number of employee de-creased during 1994 and 1995 due to the decreasing demand. 3) Over the half of the textile firms produced raw fabric products while only 20% of them were involved in additional dyeing and /or printing finish which re-sulted in low value added production 4) The R&D effort of the textile indus-try for the Korean traditional clothes was very low due to the market uncertainty lack of technological knowledge and most of all small size of the firms 5) Most raw materials for the textile in -dustry were imported with high(25%) tariff rates resulting in price increase and thus low competitiveness in the market. 6) The textile producers sole about the 70% of their products to the wholesalers while selling the rest to the retailers di-rectly. This showed the dual structure of the distribution channel in the textile products. These results suggested some implica-tions for the firms the policy makers and the researchers. The firms should develop new and improved products to increase and create consumer demand by intensive R&D efforts. The government policy ma-kers should give financial supports the firms with R&D investment and legal help such as lowing tariff rate for the raw ma-terials. The researchers from the academy could help the textile industry with the advanced technological knowledge and up-date information for the consumer fashion demand.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.6
• /
• pp.537-544
• /
• 2004

An effective methodology is reported for determining the optimal lot size of batch processing and storage networks which include uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, infernally consumed, transported to or from other sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sires while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of the global supply chain.