• Asia pacific journal of information systems
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• v.23 no.2
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• pp.67-86
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• 2013

This study first provides a stylized model that captures the essential features of the SC (Social Commerce) business and the competition process. The model focuses on the relationship between key decision issues such as marketing inputs and market value. As more SCs join the industry, they are inevitably faced with fierce competition, which may lead to sharp increase in the total marketing and advertising expenditure. This type of competition may lead the industry away from its optimal development path, and at worst, toward a disruption of the entire industry ecosystem. Such being the case, another goal of this study is to examine the possibility that the ToC (Tragedy of the Commons) may occur in the SC industry. We build game models, each of which assumes homogeneity and heterogeneity of SC providers, respectively, and derive explicit equilibrium solutions from both models. Our basic analysis presents Nash equilibria in both models and shows that SC providers are inevitably faced with fierce competition, which may lead to sharp increase in the total marketing expenses. We also compare the game outcomes with one with a hypothetical social planner who determines the total marketing level that optimizes the entire market value. Then, ToC can be defined to describe the situation where the total marketing efforts exceed the socially optimal level of marketing efforts. In both models, we examine the possibility of the ecosystem disruption and specify the conditions under which ToC may occur. However, the chance of avoiding ToC is higher with heterogeneous players than with homogeneous players. To supplement our analytical results, we develop a simulation model which incorporates a market dynamics based on the gap between actual marketing efforts and socially optimal marketing level. Simulation experiments present some lessons and insights which also confirm out findings from equilibrium analysis. For example, heterogeneity in SC providers alleviates the severity of ToC and makes it faster for survivors to escape from the ToC trap. As a result, the degree of industrial concentration tends to increase, which also explains the 'rich-get-richer' phenomenon observed in some empirical studies on the SC industry. Lastly, based on our analytical and experimental results, we come up with some measures to avoid ToC and overcome the shortcomings intrinsic to the current business model. And further discussions provide strategic implications and policy directions to overcome the possible trap of ToC in this ecosystem, and eventually help the industry to sustainably develop itself toward the next level. To name a few examples of policy measures, regulations on the marketing activities so that the overall marketing expenses cannot go beyond the socially optimal level; institutional guidelines and rules to straightening up the distortions in the way that SC providers view the marketing costs (the current marketing costs are underestimated, thereby encouraging SC providers to increase marketing expenditure); and so on.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.3
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• pp.202-211
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• 2023

The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard's production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.

• Journal of Distribution Science
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• v.14 no.11
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• pp.49-60
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• 2016

Purpose - This paper presents how Multinational Enterprises (MNEs) operate in different tax jurisdiction could decide on its transfer pricing strategy as the optimal solution to increase their global after tax income through transfer pricing and solve their related transfer pricing issues related to distribution cost, consumer, and wholesale vendor. It has been strategy issues for an MNEs to locate its tax basis of wholesale vendor and buyer in a jurisdiction where effective rather low Research design, data, and methodology - The collection of information and data for this research project gathered from various sources of secondary data. The findings of these relevant research topic article and journal were the main source of references for this research project Results - The achievement of management's operational and financial objectives depends on transfer pricing policies availability that is consistent and supports both vendor, wholesaler, distributor and ensuring sufficient documentation and data is available to support the application and arriving at the arm length. Conclusions - The study concluded with an emphasis on the importance of web-designed information about international taxation rules and transfer pricing policy and pricing agreement among wholesale vendor and whole buyer around the world.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.839-845
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• 2013

A CSTR (Continuous Stirred Tank Reactor) is a highly nonlinear process with varying parameters during operation. Therefore, tuning of the controller and determining the transition policy of controller parameters are required to guarantee the best performance of the CSTR for overall operating regions. In this paper, a methodology employing the 2DOF (Two-Degree-of-Freedom) PID controller, the anti-windup technique and a fuzzy gain scheduler is presented for the temperature control of the CSTR. First, both a local model and an EA (Evolutionary Algorithm) are used to tune the optimal controller parameters at each operating region by minimizing the IAE (Integral of Absolute Error). Then, a set of controller parameters are expressed as functions of the gain scheduling variable. Those functions are implemented using a set of "if-then" fuzzy rules, which is of Sugeno's form. Simulation works for reference tracking, disturbance rejecting and noise rejecting performances show the feasibility of using the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.157-164
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• 2015

An efficient retrieval of useful information is a prerequisite of an optimal decision making system. Hence, A research of data mining techniques finding useful patterns from the various forms of data has been progressed with the increase of the application of Big Data for convergence and integration with other industries. Each technique is more likely to have its drawback so that the generalization of retrieving useful information is weak. Another integrated technique is essential for retrieving useful information. In this paper, a uncertainty measure of information is calculated such that algebraic probability is measured by Bayesian theory and then information entropy of the probability is measured. The proposed measure generates the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Hence, the optimal decision rules are induced. Through simulation deciding contact lenses, the proposed approach is compared with the equivalence and value-reduct theories. As the result, the proposed is more general than the previous theories in useful decision-making.

• Environmental and Resource Economics Review
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• v.15 no.5
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• pp.827-858
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• 2006

The paper presents game theoretic models for self-enforcing coalition formation in order to sustain effective international environmental agreements(IEAs). The model analyzes how the intrinsically strategic nature of a government's environmental policies(the emission allowance standard) calls for rules to sustain an IEA. Focusing on the recent theoretical developments in the infinitely repeated game, the paper introduces some mechanisms to show how self-interested sovereign countries are cooperatively able to maintain an IEA rather than defect to initially profit at the expense of a pollution heaven later on. For a more realistic case needed to sustain an IEA, an optimal international environmental policy with both signatories and non-signatories under imperfect monitoring is also explored. In this extension of the model, the derivation process for a critical discount factor, a trigger price level and the length of punishment period is briefly discussed.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1054-1061
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• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map

• KDI Journal of Economic Policy
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• v.46 no.1
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• pp.91-129
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• 2024

The Bank of Korea, through a legal amendment in 2011 following the financial crisis, was entrusted with the additional responsibility of financial stability beyond its existing mandate of price stability. Since then, concerns have been raised about the prolonged increase in household debt compared to income conditions, which could constrain consumption and growth and increase the possibility of a crisis in the event of negative economic shocks. The current accumulation of financial imbalances suggests a critical period for the government and central bank to be more vigilant, ensuring it does not impede the stable flow of our financial and economic systems. This study examines the applicability of the Integrated Inflation Targeting (IIT) framework proposed by the Bank for International Settlements (BIS) for macro-financial stability in promoting long-term economic stability. Using VAR models, the study reveals a clear increase in risk appetite following interest rate cuts after the financial crisis, leading to a rise in household debt. Additionally, analyzing the central bank's conduct of monetary policy from 2000 to 2021 through DSGE models indicates that the Bank of Korea has operated with a form of IIT, considering both inflation and growth in its policy decisions, with some responsiveness to the increase in household debt. However, the estimation of a high interest rate smoothing coefficient suggests a cautious approach to interest rate adjustments. Furthermore, estimating the optimal interest rate rule to minimize the central bank's loss function reveals that a policy considering inflation, growth, and being mindful of household credit conditions is superior. It suggests that the policy of actively adjusting the benchmark interest rate in response to changes in economic conditions and being attentive to household credit situations when household debt is increasing rapidly compared to income conditions has been analyzed as a desirable policy approach. Based on these findings, we conclude that the integrated inflation targeting framework proposed by the BIS could be considered as an alternative policy system that supports the stable growth of the economy in the medium to long term.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.343-349
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• 2017

Combat modeling and simulation (M&S) of large-scale computer generated forces (CGFs) enables the development of even the most sophisticated strategy of combat warfare and the efficient facilitation of a comprehensive simulation of the upcoming battle. The DEVS-POMDP framework is proposed where the DEVS framework describing the explicit behavior rules in military doctrines, and POMDP model describing the autonomous behavior of the CGFs are hierarchically combined to capture the complexity of realistic world combat modeling and simulation. However, it has previously been well documented that computing the optimal policy of a POMDP model is computationally demanding. In this paper, we show that not only can the performance of CGFs be improved by an efficient POMDP tree search algorithm but CGFs are also able to conveniently learn the behavior model of the enemy through case studies in the scenario of counterfire warfare and the scenario of a mechanized infantry brigade's offensive operations.