• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.21-29
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• 2015

A steady-state controllable M/G/1 queueing model operating under the {T:Min(T,N)} policy is considered where the {T:Min(T,N)} policy is defined as the next busy period will be initiated either after T time units elapsed from the end of the previous busy period if at least one customer arrives at the system during that time period, or after T time units elapsed without a customer' arrival, the time instant when Nth customer arrives at the system or T time units elapsed with at least one customer arrives at the system whichever comes first. After deriving the necessary system characteristics including the expected number of customers in the system, the expected length of busy period and so on, the total expected cost function per unit time for the system operation is constructed to determine the optimal operating policy. To do so, the cost elements associated with such system characteristics including the customers' waiting cost in the system and the server's removal and activating cost are defined. Then, procedures to determine the optimal values of the decision variables included in the operating policy are provided based on minimizing the total expected cost function per unit time to operate the queueing system under considerations.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.273-284
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• 2007

This paper studies a periodic review inventory model with an e-MarketPlace transaction in reconfigurable manufacturing system(RMS). A decision maker can expand/reduce production capacity/quantities and/or replenish/dispose inventories from/to e-MarketPlace urgently to satisfy the stochastic demands. If inventories are replenished or disposed through e-MarketPlace, this leadtime is shorter than the production leadtime, but unit purchasing or selling cost is more expensive than that of expanding capacity or reducing production quantities respectively. Henceforth, trade-off on these alternatives is considered. In addition to this, in order to consider the economy of scale, our model includes the fixed cost for purchasing from e-MarketPlace and capacity expansion. We use dynamic programming and K convexity methods to characterize the nature of the optimal policy. Finally, We present the optimal inventory control policy which is composed by the combinations of a base stock and (s,S) type policy.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.103-113
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• 2019

The purpose of this study is to recognize the importance of sustaining the coal industry in spite of the declaration of new climate change regime. Due to the importance to have the ability to control the supply and demand for energy source, this study will present the ground supporting the need to save some fixed amount of coal to carry out this task. The relative quantity of fossil fuel like coal and oil consumed as an energy source is reduced due to the increasing portion of renewable energy sources. Nevertheless, we can verify the fact that the position as a main energy source in demand for and supply of electric power is still valid. And the optimal amount of coal storage is estimated through the average annual amount of demand derived from preceding studies. In this context, it is very urgent problem to maintain the coal industry as a industrial policy for the sustainable national economic growth through the coal storage policy and to determine the optimal amount of annual storage.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.39-47
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• 1992

This paper is concerned with cost analysis and determination of warranty period in a stepdown warranty policy. Manufacturer's warranty cost is analyzed for nonrepairable item, where the warranty is assumed to be renewed at any failure within the warranty period. It is shown that from this result in free replacement policy, pro-rata policy and hybrid policy can be easily calculated. The method of determining optimal warranty period is also explored.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.1-13
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• 2009

In this paper, we develop theoretical game models to determine the level of government subsidies for banks to provide policy loans to Innovative SMEs(small and medium sized enterprises) through banks, which otherwise would not finance them for the sake of their own profitability. For this, we compare net cash flows of each bank using different strategies against high risk innovative SMEs. A bank can decide whether to provide them loans or not In each period. Following Kim(2003)'s Infinite horizon model on the soft budget constraint, we introduce a situation in which banks compete against each other for higher net long-term payoffs from their loans to innovative SMEs and non-innovative SMEs. From the models, we show that competition among banks in general leads to a tighter decision against innovative SMEs, as a Nash equilibrium. It is not because the government bank is simply loose in providing loans, but because competition among commercial banks for fewer riskier borrowers results in tighter loan decisions against innovative SMEs. Thus, the competitive market for policy loans to innovative SMEs fails to reach the socially optimal level of loans for innovative SMMs. Commercial banks in the competitive market may require additional supports from the government to make up for the differences in their payoffs to support innovative SMEs, possibly much riskier due to moral hazards and poor discounted cash flows. The monopolistic government bank might also request such supports from the government to fund otherwise unqualified SMEs. We calculate an optimal level of governmental support for banks to guarantee funding such high-risk innovative SMEs over periods without deviating from their optimal Nash equilibrium policies.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.854-860
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• 2020

Deep neural networks have shown remarkable performance in various areas, including image classification and speech recognition. The variety of data generated by augmentation plays an important role in improving the performance of the neural network. The transformation of data in the augmentation process makes it possible for neural networks to be learned more generally through more diverse forms. In the traditional field of image process, not only new augmentation methods have been proposed for improving the performance, but also exploring methods for an optimal augmentation policy that can be changed according to the dataset and structure of networks. Inspired by the prior work, this paper aims to explore to search for an optimal augmentation policy in the field of sound data. We carried out many experiments randomly combining various augmentation methods such as adding noise, pitch shift, or time stretch to empirically search which combination is most effective. As a result, by applying the optimal data augmentation policy we achieve the improved classification accuracy on the environmental sound classification dataset (ESC-50).

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.174-176
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• 2005

This treatise studies composing process that renew information mastered by interactive experience between environment and system via network among individuals. In the previous study map information regarding free space is learned by using of reinforced learning algorithm, which enable each individual to construct optimal action policy. Based on those action policy each individuals can obtain optimal path. Moreover decision process to distinguish best optimal path by comparing those in the network composed of each individuals is added. Also information about the finally chosen path is being updated. A self renewing method of each system information by sharing the each individual data via network is proposed Data enrichment by shilling the information of many maps not in the single map is tried Numerical simulation is conducted to confirm the propose concept. In order to prove its suitability experiment using micro-mouse by integrating and comparing the information between individuals is carried out in various types of map to reveal successful result.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.37-52
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• 1998

In many distribution systems important cost reductions and/or service improvements may be achieved by adopting an efficient inventory policy and proper selection of facilities. These efficiency improvements and service enhancements clearly require an integrated approach towards various logistical planning functions. The areas of inventory control and transportation planning need to be closely coordinated. The purpose of this paper is to construct an integrated model that can minimize the total cost of the transportation and inventory systems between multiple origin and destination points, where in origin point i has the supply of commodities and in destination point j requires the commodities. In this case, demands of the destination points are assumed random variables which have a known probability distribution. Using the lot-size reorder-point policy and the safety stock level that minimize total cost we find optimal distribution centers which transport the commodities to the destination points and suggest an optimal inventory policy to the selected distribution center. We also show if a demand greater than one unit will occur at a particular time, we describe the approximate optional replenishment policy from computational results of this lot-size reorder-point policy. This model is formulated as a 0-1 nonlinear integer programming problem. To solve the problem, this paper proposes heuristic computational procedures and a computer program with UNIX C language. In the usefulness review, we show the meaning and validity of the proposed model and exhibit the results of a comparison between our approach and the traditional approach, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.50-59
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• 2014

In this paper, we develop an efficient approach to solve a multiple-item budget-constraint newsboy problem with a reservation policy. A conventional approach for solving such problem utilizes an approximation for the evaluation of an inverse of a Gaussian cumulative density function when the argument of the function is small, and a heuristic method for finding an optimal Lagrangian multiplier. In contrast to the conventional approach, this paper proposes more accurate method of evaluating the function by using the normalization and an effective numerical integration method. We also propose an efficient way to find an optimal Lagrangian multiplier by proving that the equation for the budget-constraint is in fact a monotonically increasing function in the Lagrangian multiplier. Numerical examples are tested to show the performance of the proposed approach with emphases on the behaviors of the inverse of a Gaussian cumulative density function and the Lagrangian multiplier. By using sensitivity analysis of different budget constraints, we show that the reservation policy indeed provides greater expected profit than the classical model of not having the reservation policy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.39-46
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• 2011

This study is conducted to examine the optimal integrated security policy based on network in case of attacks by implementing unique security policies of various network security equipments as an algorithm within one system. To this end, the policies conduct the experiment to implement the optimal security system through the process of mutually integrating the unique defense policy of Firewall, VPN(Virtual Private Network), IDS(Intrusion Detection System), and IPS(Intrusion Prevention System). In addition, this study is meaningful in that it designs integrated mechanism for rapid detection of system load caused by establishment of the security policy and rapid and efficient defense and secures basic network infrastructure implementation.