• 대한산업공학회지
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• 제23권3호
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• pp.469-485
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• 1997

자동생산화를 위한 공장설계에 있어서 설비배치에 관한 연구는 제조 유연성 요소의 통합과 이용을 적절하게 수행함으로써 가능하다. 특히 현실적인 설비배치의 특성은 그룹테크놀로지와 물자흐름의 전략을 파악하고 그들의 방법을 조사함으로써 기존의 연구에서 이론적으로 치우치는 경향을 몇 가지의 방법을 통합함으로 실질적인 응용에 그 목적을 두고 있다. 본 연구는 그래프이론과 수학적인 모형을 개발하여 통합적인 접근방법을 전개한다. 또한 설비배치 디자인에 대한 평가를 정량적인 방법으로 나타내고 있으며 경영전략에 있어 제조설비능력을 제고하는데 그 응용성을 보여주고 있다. 그것은 자동화 생산환경에 있어 각 시스템의 응용성과 목적과 관계 그리고 물자흐름관계 등을 정확하게 반영하는데 이바지한다. 현대 제조산업에 있어 고려할 수 있는 모든 각각의 제조요소가 제 특성을 수행하기 위해서는 우선적으로 설비배치 디자인의 중요함을 예를 들어 보여준다.

• Toxicological Research
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• 제17권
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• pp.241-249
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• 2001

Toxicology is under challenge from several new trends in science and technology, namely computer, the Internet, genome projects, genomic technologies, and combinatorial chemistry. These new trends will drastically change research style of toxicology. In addition to conventional uni cellular tests and animal tests using rodents, computer simulation, DNA chips (microarrays), in vivo tests using simple model organisms such as nematodesor flies become important routine screening tests. How to arrange these tests in tiers will become a new problem. Endocrine disruptors hypothesis is a good example for this kind of futuristic approach. Computer, particularly the Internet, is also enabling toxicologists and regulatory experts to collaborate more closely. The IPCS (International Program for Chemical Safety) which is ajoint project of WHO, ILO and UNEP, is a well-known international collaborative research for chemical risk assessments. The GINC project of IPCS is an effort to utilize the Internet for such collaborations. Some efforts were also made to establish regional collaboration network in East Asia under this project.

• 한국경영과학회지
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• 제26권2호
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• pp.59-68
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• 2001

The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(D)
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• pp.300-302
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• 2012

In this paper, we investigate the performance of multi-channel cognitive radio networks (CRNs) by taking into consideration the problem of channel assignment and link scheduling. We assume that secondary nodes are equipped with multiple radios and can switch among multiple channels. How to allocate channels to links and how much power used on each channel to avoid mutual interference among secondary links are the key problem for such CRNs. We formulate the problem of channel assignment and link scheduling as a combinatorial optimization problem. Then, we propose a the optimal solution and show that it converges to maximum optimum in some iterations by using numerical results.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.617-620
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• 2000

In this paper, we show an application of constraint logic programming to the operation scheduling on machines in a job shop. Constraint logic programming is a new genre of programming technique combining the declarative aspect of logic programming with the efficiency of constraint manipulation and solving mechanisms. Due to the latter feature, combinatorial search problems like scheduling may be resolved efficiently. In this study, the jobs that consist of a set of related operations are supposed to be constrained by precedence and resource availability. We also explore how the constraint solving mechanisms can be defined over a scheduling domain. Thus the scheduling approach presented here has two benefits: the flexibility that can be expected from an artificial intelligence tool by simplifying greatly the problem; and the efficiency that stems from the capability of constraint logic programming to manipulate constraints to prune the search space in an a priori manner.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.21-28
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• 2005

This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

• ETRI Journal
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• 제39권1호
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• pp.1-12
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• 2017

In this paper, we present a new approach for the progressive compression of three-dimensional (3D) mesh geometry using redundant frame dictionaries and sparse approximation techniques. We construct the proposed frames from redundant linear combinations of the eigenvectors of a combinatorial mesh Laplacian matrix. We achieve a sparse synthesis of the mesh geometry by selecting atoms from a frame using matching pursuit. Experimental results show that the resulting rate-distortion performance compares favorably with other progressive mesh compression algorithms in the same category, even when a very simple, sub-optimal encoding strategy is used for the transmitted data. The proposed frames also have the desirable property of being able to be applied directly to a manifold mesh having arbitrary topology and connectivity types; thus, no initial remeshing is required and the original mesh connectivity is preserved.

• 호남수학학술지
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• 제40권1호
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• pp.27-46
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• 2018

In this paper, (E, L)-preproximity and uniformity spaces in matriod theory as a generalized to a classical proximity and Uniformity spaces introduced by Csaszar [1] is introduced. Recently, Shi [17]-[18] introduced a new approach to the fuzzification of matroids.Here introduce (E, L)-preproximity and uniformity spaces, Uniformity and strong uniformity on (E, L)-fuzzifying matroid space, Not only study the properties of this new notions, but it has been generated (E, L)-fuzzifying matroid Space from (E, L)-preproximity and uniformity spaces. Next to introduced (E, L)-preproximity continuous in (E, L)-fuzzifying matroid space and used it in more properties. Finally we solve combinatorial optimizations problem via (E, L)-fuzzifying matroid space.

• 대한기계학회논문집
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• 제19권8호
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• pp.1854-1868
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• 1995

An effective nesting algorithm has been proposed to allocate the arbitrary shapes on one or several raw sheets by applying the well-known simulated annealing algorithm as the optimization technique. In this approach, both the shapes to be allocated and the raw sheets are represented as the grid-based models. This algorithm can accommodate every possible situations encountered in cutting apparel parts from the raw leather sheets. In other words, the usage of the internal hole of a shape for other small shapes, handling of the irregular boundaries and the interior defects of the raw sheets, and the simultaneous allocation on more than one raw sheets have been tackled on successfully in this study. Several computational experiments are presented to verify the robustness of the proposed algorithm.

• 한국국방경영분석학회지
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• 제35권3호
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• pp.47-59
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• 2009

A hybrid Genetic Algorithm is applied to military resource allocation problem. Since military uses many resources in order to maximize its ability, optimization technique has been widely used for analysing resource allocation problem. However, most of the military resource allocation problems are too complicate to solve through the traditional operations research solution tools. Recent innovation in computer technology from the academy makes it possible to apply heuristic approach such as Genetic Algorithm(GA), Simulated Annealing(SA) and Tabu Search(TS) to combinatorial problems which were not addressed by previous operations research tools. In this study, a hybrid Genetic Algorithm which reinforces GA by applying local search algorithm is introduced in order to address military optimization problem. The computational result of hybrid Genetic Algorithm on Missile Interceptor Allocation problem demonstrates its efficiency by comparing its result with that of a simple Genetic Algorithm.