• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.394-401
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• 2003

We consider a PCB grouping problem arising from the electronic industry. Given a surface mounting device, several types of PCBs and a number of component feeders used to assemble the PCBs. the optimization problem is the PCB grouping problem while minimizing setup time of component feeders. We formulate the problem as an Integer programming model and propose a column generation approach to solve the Integer programming formulation. In this approach we decompose the original problem Into master problem and column generation subproblem Starting with a few columns in the master problem. we generate new columns successively by solving subproblem optimally. To solve the subproblem. we use a branrh-and-rut approach. Computational experiments show that our solution approach gives high quality solutions in a reasonable computing time.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.381-384
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• 2002

The free surface flow problem has been one of the most interesting and challenging topic in the area of the naval ship hydrodynamics and ocean engineering field. The problem has been treated mainly in the scope of the potential theory and its governing equation is well known Laplace equation. But in general, the exact solution to the problem is very difficult to obtain because of the nonlinearlity of the free surface boundary condition. Thus the linearized free surface problem has been treated often in the past. But as the computational power increases, there is a growing trend to solve the fully nonlinear free surface problem numerically. In the present study, a time-dependent finite element method is developed to solve the problem. The initial-boundary problem is formulated and replaced by an equivalent variational formulation. Specifically, the computations are made for a highly nonlinear flow phenomena behind a transom stern ship and a vertical strut piercing the free surface.

• Journal of the Korean Applied Science and Technology
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• v.40 no.1
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• pp.168-178
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• 2023

This study aims to develop and apply problem Based learning classes through skin beauty basics and practical classes to foster experts close to the beauty care field. Students are presented with a problem to solve in practice in the field of skin care, and in order to solve the problem, intellectuals try to come up with a solution to solve the problem jointly. Conduct skin beauty learning classes that apply problem-based learning to students, and analyze the effects after taking the classes. As a result of this study, by presenting actual problems in the field, scientists were able to actually do what experts do, and it was possible to come up with various solutions to a given problem in the future, and to share parts to expect problem-solving abilities. These results suggest that problem-solving and the problem-centered learning process for problem-solving have a positive effect on improving his problem-solving ability.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.17-28
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• 2008

The Steiner arborescence problem is known to be NP-hard. The objective of this problem is to find a minimal Steiner tree which starts from a designated node and spans all given terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step, graph reduction rules eliminate useless nodes and arcs which do not contribute to make an optimal solution. In the second step. ant colony algorithm with use of Prim's algorithm is used to solve the Steiner arborescence problem in the reduced graph. The proposed method based on a two-step procedure is tested in the five test problems. The results show that this method finds the optimal solutions to the tested problems within 50 seconds. The algorithm can be applied to undirected Steiner tree problems with minor changes. 18 problems taken from Beasley are used to compare the performances of the proposed algorithm and Singh et al.'s algorithm. The results show that the proposed algorithm generates better solutions than the algorithm compared.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.730-735
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• 2005

Though Knapsack Problem appears to be simple, it is a NP-hard problem that is not solved in polynomial time as combinational optimization problems. To solve this problem, GA(Genetic Algorithms) was used in the past. However, there were difficulties in real experiments because the conventional method didn't reflect the precise characteristics of DNA. In this paper we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to solve problems of Knapsack Problem. ACO was applied to (0,1) Knapsack Problem; as a result, it reduced experimental errors as compared with conventional methods, and found accurate solutions more rapidly.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.769-776
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• 2003

DNA computing has used to solve MCP (Maximal Clique Problem). However, when current DNA computing is applied to MCP. it can't efficiently express vertices and edges and it has a problem that can't look for solutions, by misusing wrong restriction enzyme. In this paper we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to solve MCP's problem. We applied ACO to MCP and as a result ACO could express DNA codes of variable lengths and generate codes without unnecessary vertices than Adleman's DNA computing algorithm could. In addition, compared to Adleman's DNA computing algorithm, ACO could get about four times as many as Adleman's final solutions by reducing search time and biological error rate by 15%.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.85-86
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• 2001

3D parallel rasterizer on a single chip for high performance generates consistency problem. To solve this problem, 3D parallel rasterizer with consistency buffer Os proposed. This can simultaneously process a plurality of Primitives. Experimental results show 1.1-2.0x speedups using a simple model. This method can achieve high performance and cost effectiveness.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.308-311
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• 2008

The ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants. Recently, ACO has been widely used to solve the difficult combinatorial optimization problems. In this paper, we propose an ACO algorithm to solve the two disjoint paths problem with dual link cost structure (TDPDCP). We propose a dual pheromone structure and a procedure for solution construction which is appropriate for the TDPDCP. Computational comparisons with the state-of-the-arts algorithms are also provided.

• Journal of the Korean Society for Railway
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• v.8 no.5
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• pp.391-397
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• 2005

In this paper we present a model for the maintenance routing construction and the trainset allocation (maintenance routing problem). The model solves the maintenance routing problem using column generation algorithm which was used to combine constraint programming and linear programming. Ilog-cplex was used to solve the linear programming model and Ilog-solver was used to solve the constraint programming model. The computational experience is also provided.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.04a
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• pp.57-66
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• 2000

Production-distribution planning is the most important part in supply chain management (SCM). To solve this planning problem, either analytic or simulation approach has been developed. However these two approaches have their own demerits in problem solving. In this paper, we propose a hybrid approach which is a specific problem solving procedure combining analytic and simulation method to solve production-distribution problems in supply chain. The machine capacity and distribution capacity constraints in the analytic model are considered as stochastic factors and adjusted by the proposed specific process according to the results from independently developed simulation model which includes general production-distribution characteristics.