• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.27-34
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• 2023

The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

• Management Science and Financial Engineering
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• v.17 no.2
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• pp.99-129
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• 2011

This paper analytically studies how to choose hedging instrument for firms with steady operating cash flows from value maximization perspective. I derive a formula to determine option's optimal strike that makes hedged cash flow have the best monetary payoff given a hedger's view on the underlying asset. I find that not only the expected mean but also the expected standard deviation of the underlying asset in relation to the forward price and the implied volatility play a crucial role in making optimal hedging decision. Higher moments play a certain part in hedging decision but to a lesser degree.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.195-204
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• 2014

The transition from fossil to renewable energy is inevitable due to fossil depletion. So, Renewable energy is very important for energy security and economic growth although it's R&D is long-term and high risky project. We propose new valuation method which combined system dynamics and compound real option method for long-term and high risk projects such as renewable energy. This method can show dynamic valuation results for the complex causal interaction and be easy for Monte-Carlo simulation to estimate volatility. And it can reflect the value of flexible decision for uncertainty. We applied the empirical analysis for Korea's photovoltaic industry by using this method. As results by empirical analysis, photovoltaic's R&D has high valuation using this method compared by traditional valuation methods such as DCF.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• Journal of Korea Technology Innovation Society
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• v.8 no.1
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• pp.29-50
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• 2005

Real options provide a new and productive way to view corporate r&d investment decisions. DCF approach is well established and beloved of financial executives, but is known to systematically underestimate investment value under significant uncertainty. Though real options are not inherent in a r&d investment, they can be used to compute the investment value including managerial flexibility like option value. In this paper, we explain how the interval of option value in black-scholes model can be estimated using simulation. We also present a process framework for interval estimation of volatility and efficient of period of investment value. In such a setting, we can obtain the appropriate interval estimation of the expanded investment value.

• Journal of Korea Technology Innovation Society
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• v.7 no.3
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• pp.533-554
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• 2004

Real option valuation considers the managerial flexibility to make ongoing decisions regarding implementation of investment projects and deployment of real assets. The appeal of the framework is natural given the high degree of uncertainty that firms face in their technology investment decisions. This paper suggests an algorithm for estimating volatility of logarithmic cash flow returns of real asset based on Monte Carlo simulation. This research uses a binomial model to obtain point estimate of real option value with embedded expansion option case and provides also an array of numerical results to show the interval estimation of option value using Monte Carlo simulation.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.90-97
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• 2021

Saemangeum Development is the largest national project in South Korea, which has been developed for an agricultural, economic and tourist area for 30 years from 1987. In order to convert power sources that used to depend on nuclear and thermal power to eco-friendly for carbon reduction, the government plans to construct a 2.1GW floating photovoltaic project by investing 4.6 trillion won, as a public-private project. For success of the Saemangeum floating photovoltaic project, economic feasibility should be checked. This study defined the factors (construction cost, electricity selling price, power generation and maintenance cost) that give a effect to the volatility of the floating photovoltaic payoffs, and analyzed the volatility of payoffs during 20 years operation period. NPV and option value of the project were calculated by applying an option to abandon. According to NPV analysis, it is determined that projects are difficult to invest. But this project has economic feasibility through real options analysis. This study is expected to help decision-makers in the economic analysis of floating photovoltaic projects by using the real options analysis.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.1-28
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• 2018

With the growing number of emerging carmakers, automotive parts manufacturers have to penetrate into emerging markets. They can provide large existing carmakers with fully customized parts because of economies scale, but cannot do this for small emerging carmakers due to their small and highly volatile volume order. Once the order by an emerging carmaker is placed, a part manufacturer is exposed to high risks both of decrease in profit margin and high opportunity cost. The platform-based mass customization can be a solution for cost reduction, but the risks of volatility in volume hard to manage. Tackling this issue, we presents a method of optimizing the product portfolio to maximize profits while managing volatility of volume order by emerging carmakers at an affordable level. It is the first robust product portfolio method to keep the scaled deviation of profits at a fixed level under volume order uncertainty. Also, the effect of on the platform-based mass customization on cost is considered. This model can be a building block of conservative market penetration as well as product development strategy while minimizing the financial risks. We conducted an empirical study of a part manufacturer targeting on eighteen automobile manufacturers in North America, Europe and Asia with it powered lift gate.

• Korean Journal of Construction Engineering and Management
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• v.18 no.2
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• pp.91-98
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• 2017

Due to climate change, countries around the world are actively investing in renewable energy, reducing fossil fuel use. 23.7% of world electricity is supplied by renewable energy. As the technology continues to develop, it is in a level to compete in terms of power generation cost, and investment conditions are improving. However, investment in renewable energy projects is not easy. This study analyzed trends of domestic and international researches on economics assessment applying real options analysis to investment decisions of hydro, solar, and wind power projects, which account for a large portion of renewable energy. This study provides (1) the difference between the traditional economic method and the real options analysis, (2) the application process, and (3) the uncertainty elements and option type of the renewable energy project presented by many studies. The real options analysis is suitable for the detailed investment strategy by considering the uncertainties of the renewable energy project and applying the option to improve the profit or to avoid the risk.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.33-53
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• 2021

In the crossroads of social change caused by the spread of the Fourth Industrial Revolution and the prolonged COVID-19, the Korean government announced the Digital New Deal policy on July 14, 2020. The Digital New Deal policy's primary goal is to create new businesses by accelerating digital transformation in the public sector and industries around data, networks, and artificial intelligence technologies. However, in a rapidly changing social environment, information asymmetry of the future benefits of technology can cause differences in the public's ability to analyze the direction and effectiveness of policies, resulting in uncertainty about the practical effects of policies. On the other hand, the media leads the formation of discourse through communicators' role to disseminate government policies to the public and provides knowledge about specific issues through the news. In other words, as the media coverage of a particular policy increases, the issue concentration increases, which also affects public decision-making. Therefore, the purpose of this study is to verify the dynamic relationship between the media coverage and the stock market on the Korean government's digital New Deal policy using Granger causality, impulse response functions, and variance decomposition analysis. To this end, the daily stock turnover ratio, daily price-earnings ratio, and EWMA volatility of digital technology-based companies related to the digital new deal policy among KOSDAQ listed companies were set as variables. As a result, keyword search volume, daily stock turnover ratio, EWMA volatility have a bi-directional Granger causal relationship with media coverage. And an increase in media coverage has a high impact on keyword search volume on digital new deal policies. Also, the impulse response analysis on media coverage showed a sharp drop in EWMA volatility. The influence gradually increased over time and played a role in mitigating stock market volatility. Based on this study's findings, the amount of media coverage of digital new deals policy has a significant dynamic relationship with the stock market.