• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.165-171
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• 2014

We develop an optimization algorithm for a periodic review inventory system under a stochastic budget constraint. While most conventional studies on the periodic review inventory system consider a simple budget limit in terms of the inventory investment being less than a fixed budget, this study adopts more realistic assumption in that purchasing costs are paid at the time an order is arrived. Therefore, probability is employed to express the budget constraint. That is, the probability of total inventory investment to be less than budget must be greater than a certain value assuming that purchasing costs are paid at the time an order is arrived. We express the budget constraint in terms of the Lagrange multiplier and suggest a numerical method to obtain optional values of the cycle time and the safety factor to the system. We also perform the sensitivity analysis in order to investigate the dependence of important quantities on the budget constraint. We find that, as the amount of budget increases, the cycle time and the average inventory level increase, whereas the Lagrange multiplier decreases. In addition, as budget increases, the safety factor increases and reaches to a certain level. In particular, we derive the condition for the maximum safety factor.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.63-76
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• 2016

Multiperiod portfolio selection problem attracts more and more attentions because it is in accordance with the practical investment decision-making problem. However, the existing literature on this field is almost undertaken by regarding security returns as random variables in the framework of probability theory. Different from these works, we assume that security returns are uncertain variables which may be given by the experts, and take absolute deviation as a risk measure in the framework of uncertainty theory. In this paper, a new multiperiod mean absolute deviation uncertain portfolio selection models is presented by taking transaction costs, borrowing constraints and threshold constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on uncertain theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. To solve the new model in general cases, the forward dynamic programming method is presented. In addition, a numerical example is also presented to illustrate the modeling idea and the effectiveness of the designed algorithm.

• Journal of Agricultural Extension & Community Development
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• v.28 no.4
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• pp.167-174
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• 2021

This study analyzed the feasibility of custom work service to deal with the imbalance of farm labor supply due to population aging. The economic feasibility analysis is based on the case of Bonghwang-myeon in Naju-si, where the majority of farm work is entrusted to local agricultural cooperative. To assess the project profitability and economic feasibility based on the projected cash flow for the next ten years, Return On Investment (ROI), Net Present Value (NPV), and Internal Rate of Return (IRR) of the projects were calculated. The results showed that ROI is estimated at 13.7%, and NPV and IRR are KRW 1,504,932,000 and 15.6%, respectively, with a discount rate of 4.5%, indicating a good enough profitability. Furthermore, a sensitivity analysis with government support as part of an assumption showed that without the support, NPV turns negative, implying that the project is not profitable, and that government support for at least 30% of the cost is needed to secure the economic feasibility of a project. Hence, to promote agricultural work entrustment, it is necessary for the government to partly support the agricultural machinery and facility costs, which require a considerable amount of initial investment.

• Journal of KIBIM
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• v.11 no.3
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• pp.34-44
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• 2021

This study quantitatively analyzes the work performance of the structural safety diagnosis team that diagnoses pipe racks. To this end, a method for evaluating the performance of the structural safety diagnosis team using the queuing model was proposed. For verification, the case of applying the existing method and the method of introducing a 3D laser scanner for one site was used. The period, number of people, and initial investment cost of each project were collected through interviews with case project experts. As a result of analyzing the performance of the structural safety diagnosis team using the queuing model, it was possible to confirm the probability of delay in the work of each project and the amount of delayed work. Through this, the cost (standby cost) when the project was delayed was analyzed. Finally, economic analysis was conducted in consideration of the waiting cost, labor cost, and initial investment cost. The results of this study can be used to decide whether to introduce 3D laser scanners.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.11-17
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• 2021

This study aims to know the contribution of external debt to Indonesia's economic growth. The data used a source from the Central Bank of Indonesia from 2011 to 2020. This empirical study uses a quantitative approach with Error Correction Model as the regression method. Government expenditure, government revenue, export, import, inflation, and exchange rate are control variables. The result of the descriptive statistic shows economic growth in Indonesia increased gradually from 2011 to 2020. The increase in economic growth occurred regardless of the contribution of external debt. It does, however, inform the public that Indonesia's economic system has seen successful investments. The result of the study is classified into long-term and short-term. External debt contributes to growth in the long term and has a significant impact. The study's findings will give Indonesia optimism that it can manage external debt as a source of domestic investment. This research may also persuade Indonesia to maintain its economic potency in the future. In the future, this research can be perfected, by adding a threshold level on the amount of Indonesia's external debt.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Journal of Korea Trade
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• v.26 no.5
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• pp.39-55
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• 2022

Purpose - This study empirically examines the effect of the Korean government export promotion program (EPP) on small and medium-sized enterprise (SMEs) export performance using firm-level data. Unlike most previous studies that investigated some specific samples of firms, this study analyzes a vast amount of SME data of the Korean Small and Medium Business Administration over the period 2005 to 2008. Design/methodology - An endogeneity problem arises when a firm's probability of being selected is correlated with the likelihood of successfully implementing EPPs. To control for the endogeneity of the EPPs in a relatively short-period sample, we employ 2-Stage Residual Inclusion (2SRI) RE-Tobit and bivariate Tobit procedure. Findings - Analyses show that Korean government EPPs have positive significant effects on SME exports. Empirical results also show that SME export activities are significantly encouraged by R&D investment and capital intensity, but not obviously by labor productivity. Originality/value - This study provides evidence that SME capital intensity, R&D investment, and the number of workers are significant determinants to SME exporting activities, whereas per worker labor cost and employee education are not. These results imply that even for SMEs, firm size is a major factor in promoting exporting activities.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.691-700
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• 2023

Purpose: This study aims to analyze the diffusion pattern of the Mongolian mobile phone market. In particular, we used a generalized diffusion model to explore the factors affecting market potenial. Methods: We used three diffusion models to estimate the number of mobile subscribers in Mongolia. Based on the Logistic model with the best fitness, we introduced time-varying market potential and explored the influence of various independent variables such as GDP and inflation. Results: Among the basic diffusion models, the Logistic model was the best in terms of estimation performance and statistical significance. The estimation results of the Generalized Logistic model confirm that investment in the telecommunication sector has a significant positive effect on market potential. The estimation of the Generalized Logistic model effectively describes the continuous growth of the Mongolian telecommunications market until recently. Conclusion: We have analyzed the diffusion pattern of the Mongolian telecommunications market and found that the amount of investment in the sector leads to the growth of the market size. This study is original in terms of its subject - Mongolian telecommunications market and methodology - time-varying market potential.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.73-81
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• 2024

As the demand for a healthy life increases and the use of information technology expands, interest in digital healthcare has increased. Among the digital healthcare technologies, digital therapeutics (DTx), which are capable of disease prevention, management, and treatment rather than simple healthcare, are expected to play a key role in future healthcare services. As interest in untact remote treatment that can minimize the risk of viral infection has rapidly increased since the spread of COVID-19, the application of DTx has received much attention because it can partially replace face-to-face treatment for mental illnesses, chronic diseases, and other diseases, reducing concerns about infection. In addition, because of the nature of software, DTx have lower toxicity and fewer side effects than existing treatments and do not require manufacturing, transportation, and storage like general medicines. Hence, they can be supplied in large quantities at low cost and have the advantage of lowering medical costs. However, despite these advantages, it has been pointed out that there are difficulties in investment and universal use because of the complexity of pricing and malpractice compensation. In other words, if it is difficult to prove and measure the improvements in disease management and treatment using DTx and it takes a considerable amount of time and money to do so, it will be difficult to attract investment from stakeholders such as medical providers and pharmaceutical companies. In this paper, we examine the domestic and global application status and development trends of DTx and determine the relevant implications.