• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2587-2600
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• 2012

It has been demonstrated that choosing an appropriate relay node can improve the transmission rate for the system. However, such system improvement brought by the relay selection may be degraded with the presence of the malicious relay nodes, which are selected but refuse to cooperate for transmissions deliberately. In this paper, we formulate the relay selection issue as a restless bandit problem with the objective to maximize the average rate, while considering the credibility of each relay node, which may be different at each time instant. Then the optimization problem is solved by using the priority-index heuristic method effectively. Furthermore, a low complexity algorithm is offered in order to facilitate the practical implementations. Simulation results are conducted to demonstrate the effectiveness of the proposed trust-based relay selection scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1758-1761
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• 1997

The problem of long range capacity expansion planing for chemical processing network under uncertain demand forecast secnarios is addressed. This optimization problem involves capactiy expansion timing and sizing of each chemical processing unit to maximize the expected net present value considering the deviation of net present values and the excess capacity over a given time horizon. A multiperiod mixed integer nonlinear programming optimization model that is both solution and modle robust for any realization of demand scenarios is developed using the two-stage stochastic programming algorithm. Two example problems are considered to illustrate the effectiveness of the model.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.715-718
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• 2009

The assembly line is one of the typical process hard to analyze with mathematical methods including even stochastic approaches, because it includes many manual operations varying drastically depending on operators' skills. In this paper, we suggest the simulation optimization method to design the optimal assembly line of a gas oven. To achieve the optimal design, firstly, we modeled the real gas oven assembly line with actual data, such as assembly procedures, operation rules, and other input parameters and so on. Secondly, we build some alternatives to enhance the line performance based on business rules and other parameters. The DOE(Design Of Experiment) techniques were used for testing alternatives under various situations. Each alternatives performed optimization process with evolution strategy; one of the GA(Genetic Algorithm) techniques. As a result, we can make about 7% of throughputs up with the same time and cost. By this process, we expect the assembly line can obtain the solution compatible with their own problems.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.106-118
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• 2007

This paper present a new optimization model to generate aggregate production planning by considering electric cost. The new Time Of Switching (TOS) electric type is introduced by switching over Time Of Day (TOD) and Time Of Use (TOU) electric types to minimize the electric cost. The fuzzy demand and Dynamic inventory tracking with multiple plant capacity are modeled to cover the uncertain demand of customer. The constraint for minimum hour limitation of plant running per one start up event is introduced to minimize plants idle time. Furthermore; the Optimal Weight Moving Average Factor for customer demand forecasting is introduced by monthly factors to reduce forecasting error. Application is illustrated for multiple cement mill plants. The mathematical model was formulated in spreadsheet format. Then the spreadsheet-solver technique was used as a tool to solve the model. A simulation running on part of the system in a test for six months shows the optimal solution could save 60% of the actual cost.

• Korean Management Science Review
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• v.18 no.2
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• pp.117-124
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• 2001

This paper presents a machining parameter selection approach using an evolutionary computation (EC). In order to perform a successful material cutting process, the engineer is to select suitable machining parameters. Until now, it has been mostly done by the handbook look-up or solving optimization equations which is inconvenient when not in handy. The main thrust of the paper is to provide a handy machining parameter selection approach. The EC is applied to rapidly find optimal machining parameters for the user\\`s specific machining conditions. The EC is basically a combination of genetic a1gorithm and microcanonical stochastic simulated annealing method. The approach is described in detail with an application example. The paper concludes with a discussion on the potential of the proposed approach.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.403-405
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• 2003

In this paper, we applied a chaos search method for feeder reconfiguration problem in unbalanced distribution system. Chaos method, in optimization problem, searches the global optimal solution on the regularity of chaotic motions and more easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos search method applied to the IEEE 13 unbalanced test feeder systems, and the test results indicate that it is able to determine appropriate switching options for global optimum configuration.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.348-351
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• 1993

The simulated annealing(SA) algorithm is a stochastic strategy for search of the ground state and a powerful tool for optimization. based on the anneal ins process used for the crystallization in physical systems. It's main disadvantage is the long convergence time. Therefore, this paper shows that the new algorithm using SA can be applied to reduce the computation time. This idea has been used to solve the estimation problem of the nonlinear parameter.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1344-1346
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• 1996

In this paper, we discuss an informal analysis of diffusion, global optimization properties of Langevine competitive learning neural network. In the view of the stochastic process, it is important that competitive learning gurantee an optimal solution for pattern recognition. We show that the binary reinforcement function in Langevine competitive learning is a brownian motion as Gaussian process, and construct the Fokker-Plank equation for the proposed neural network. Finally, we show that the informal analysis of the proposed algorithm has a possiblity of globally optimal. solution with the proper initial condition.

• Korean Journal of Hydrosciences
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• v.5
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• pp.57-70
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• 1994

For the system benefit optimization by considering risk or reliability from a multiple reservoir system using the Monte Carlo Technique, Many stochastically generated inflow series have to be used for the system analysis. In this study, the stochastically generated inflow series for the multiple reservoir system operation are preprocessed according to the considering system objectives and operating time periods. Through this procedure, several representative inflow series which have discrate probability levels and operation horizons are selected among the thousands of generated inflows. Then a deterministic optimization technique is applied to the hydropower energy estimation from the Han River Reservoir System which considers five reservoirs in this study. It took much less computational requirements than using the original Morite Carlo Technique, even though estimated result was almost similar.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.2
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• pp.133-149
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• 2022

The design of a wooden impact limiter equipped to a transportation cask for radioactive materials was optimized. According to International Atomic Energy Agency Safety Standards, 9 m drop tests should be performed on the transportation cask to evaluate its structural integrity in a hypothetical accident condition. For impact resistance, the size of the impact limiter should be properly determined for the impact limiter to absorb the impact energy and reduce the impact force. Therefore, the design parameters of the impact limiter were optimized to obtain a feasible optimal design. The design feasibility criteria were investigated, and several objectives were defined to obtain various design solutions. Furthermore, a probabilistic approach was introduced considering the uncertainties included in an engineering system. The uncertainty of material properties was assumed to be a random variable, and the probabilistic feasibility, based on the stochastic approach, was evaluated using reliability. Monte Carlo simulation was used to calculate the reliability to ensure a proper safety margin under the influence of uncertainties. The proposed methodology can provide a useful approach for the preliminary design of the impact limiter prior to the detailed design stage.