• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.47-55
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• 2010

Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.

• Korean Management Science Review
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• v.14 no.2
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• pp.69-79
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• 1997

The computational speed of interior point method of linear programming depends on the speed of Cholesky factorization. If the coefficient matrix A has dense columns then the matrix A.THETA. $A^{T}$ becomes a dense matrix. This causes Cholesky factorization to be slow. We study an efficient implementation method of the dense column splitting among dense column resolving technique and analyze the relation between dense column splitting and order methods to improve the sparsity of Cholesky factoror.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.191-193
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• 1989

This paper presents a optimal Var allocation algorithm for minimizing power loss and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A stochastic Linear Programming technique based on chance constrained method is applied to solve the probabilistic constraint. The test result in IEEE-14 Bus model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before Var allocation.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.863-865
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• 1988

This paper presents a optimal Var allocation algorithm for minimizing transmission line losses and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A Stocastic Linear programming technique based on chance constrained method is applied, to solve the var allocation problem with probabilistic constraint. The test result in 6-Bus Model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before var allocation.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.5 s.143
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• pp.534-541
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• 2005

Until now, Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how in its own. It is very useful to extract knowledge or information from the accumulated existing data by using data mining technique This paper treats an evolutionary computation based on genetic programming (GP), which can be one of the components to realize data mining. The paper deals with linear models of GP for the regression or approximation problem when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by utilizing the symbolic processing algorithm. In addition to a standard linear model consisting of mathematic functions, one variant form of a linear model, which can be built using low order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. The suggested model can be utilized as a designing tool to predict design parameters with small accumulated data.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.290-293
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• 2000

Knowledge discovery refers to the overall process of discovering useful knowledge from data. The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the DSS area. However, they rely on the strict assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the GPS algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.91-107
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• 2000

The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the Decision Support Systems area. However, they rely on the assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the General Problem Solver(GPS) algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.1
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• pp.51-58
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• 1986

The propose of this study is to presnet a generalized linear/goal programming software, which has been developed to run on mickrocomputers with at least 512 K bytes of memory. The main characteristics of our algorithm for solving LP/GP problems are outlined as follows ; First, it uses the revised simplex algorithm, which is the most efficient computational procedure for computers. Second, it employs the sparse matrix technique to overcome the limited memory of microcomputers. Last, it uses the modified product form of invers (MPFI) to reduce round-off errors. The test runs with our code written in FORTRAN show that it can be used as an effective tool for solving linear/goal programming problems of considerable size.

• Structural Engineering and Mechanics
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• v.3 no.6
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• pp.529-539
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• 1995

The rigid-plastic yield-line analysis of isotropically reinforced concrete slabs acting in conjunction with torsionally weak supporting beams is developed as the lower-bound form of a linear programming formulation. The analysis is extended to consider geometric variation of chosen yield-line patterns by the technique of sequential linear programming. A strategy is followed of using a fine potential yield-line mesh to identify possible collapse modes, followed by analysis using a coarser, simplified mesh to refine the investigation and for use in conjunction with geometric optimization of the yield-line system. The method is shown to be effective for the analysis of three slabs of varying complexity. The modes detected by the fine and simplified analyses are not always similar but close agreement in load factors has been consistently obtained.

• Korean Management Science Review
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• v.19 no.1
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• pp.67-75
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• 2002

This paper deals with the visualization method of solutions of the linear programming Problem. We used the revised simplex method for the LP algorithm. To represent the solutions at each iteration, we need the informations of feasible legion and animated effect of solutions. For the visualization in high dimension space, we used the method of Projection to the three dimensions if the decision variable vector is over three dimensions, and we studied the technique of preserving original Polyhedral information such as the number of vertices. In addtion, we studied the method of visualizing unbounded feasible region and the adjacency relationship of the vortices welch is Indispensable to cisualize feasible legion.