• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.189-197
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• 1999

Assembly line balancing problem is known as one of difficult combinatorial optimization problems. This problem has been solved with linear programming, dynamic programming approaches. but unfortunately these approaches do not lead to efficient algorithms. Recently, genetic algorithm has been recognized as an efficient procedure for solving hard combinatorial optimization problems, but has a defect that requires long-run time and computational complexties to find the solution. For this reason, we adapt a new method called the Critical Node Method that is intuitive, easy to understand, simple for implementation. Fuzzy set theory is frequently used to represent uncertainty of information. In this paper, to treat the data of real world problems we use a fuzzy number to represent the duration and Critical Node Method based heuristic procedure is developed for solving fuzzy assembly line balancing problem.

• Journal of the Korean Ceramic Society
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• v.41 no.5
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• pp.381-387
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• 2004

Recently developed Plasma Display Panels (PDP) require phosphors of high luminance at Vacuum Ultraviolet (VUV) excitation. The present investigation developed new PDP phosphors using combinatorial chemistry method. We applied T $b^{3+}$ -activated yttrium gadolinium phosphates system to our combinatorial fine-tuning technique. As a result, the optimum composition was determined to be (G $d_{0.74}$ $Y_{0.11}$T $b_{0.15}$) $P_{1.15}$ $O_{\delta}$ through the two-step combinatorial screening process including excess phosphorous and Gd replacement. We found that the sample of the optimum composition shows a higher luminescence efficiency at VUV excitation and a shorter decay time than the commercially available Z $n_2$ $SiO_4$:Mn phosphor.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1550-1556
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• 1997

In this paper, we apply the evolutionary algorithm to the combinatorial optimization problem. Evolutionary algorithm useful for the optimization of the large space problem. This paper propose a method for the reuse of wastes of light water in atomic reactor system. These wastes contain several reusable elements, and they should be carefully selected and blended to satisfy requirements as an input material to the heavy water atomic reactor system. This problem belongs to an NP-hard like the 0/1 knapsack problem. Two evolutionary strategies are used as approximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method which perform the feasible test and solution evaluation by using the vectored knowledge in problem domain. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• Applied Biological Chemistry
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• v.40 no.1
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• pp.1-11
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• 1997

• Journal of the Korean institute of surface engineering
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• v.48 no.3
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• pp.126-130
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• 2015

A high-pressure depletion method using plasma chemical vapor deposition (CVD) is often used to deposit hydrogenated microcrystalline silicon (${\mu}c-Si:H$) films of a low defect density at a high deposition rate. To understand proper deposition conditions of ${\mu}c-Si:H$ films for a high-pressure depletion method, Si films were deposited in a combinatorial way using a multi-hollow discharge plasma CVD method. In this paper the substrate temperature dependence of ${\mu}c-Si:H$ film properties are demonstrated. The higher substrate temperature brings about the higher deposition rate, and the process window of device quality ${\mu}c-Si:H$ films becomes wider until $200^{\circ}C$. This is attributed to competitive reactions between Si etching by H atoms and Si deposition.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.236-243
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• 2005

This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1998.11a
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• pp.34-37
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• 1998

Combinatorial chemistry is one of the most interested topics in the area of drug discovery. One of the most important points is how to find a lead compound that gives the seed structure for designing of a combinatorial library. Natural products is suitable for searching a new bioactive compound with new structure. We have carried out systematic screening works to find natural products possessing the effects on inter-and intra-cellular signaling. Two hundreds extracts of medical plants and two thousands microbial culture broth samples have been tested for the induction and inhibition of IL-2 or IL-6 production (Fig. 1). ELISA is an efficient method for screenings from such a large number of samples. Now, we apply this method to search prion- binding agents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.165-166
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• 2015

Recently, IoT is a hot issue in the IT research and industry area. Wireless Sensor plays a key roll in the in the IoT. Usually the device of the wireless sensor has tinyOS and small battery power. Hence there are a lot of previous research that want to extend the life time. In this paper, we will introduce the scheduling method based on the combinatorial theory.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.10a
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• pp.172-172
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• 1999

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.