• Journal of the Korean Mathematical Society
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• v.53 no.1
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• pp.19-25
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• 2016

In this paper, some omissions in Mishra and Lai [13], have been pointed out and their corrective measures have been discussed briefly.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.215-218
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• 1996

In work scheduling problems, scheduling constraints are not absolutely rigid; they may be changed depending on the scheduling aspect effected. In order to cope with changes in scheduling constraints and assignment strategies and to optimize scheduling results quickly, this paper will propose a new scheduling method which combines knowledge engineering and mathematical programming techniques.

• Bulletin of the Korean Mathematical Society
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• v.42 no.4
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• pp.837-849
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• 2005

In this paper, we obtain a successive quadratic programming algorithm for solving the semidefinite programming (SDP) relaxation of the binary quadratic programming. Combining with a randomized method of Goemans and Williamson, it provides an efficient approximation for the binary quadratic programming. Furthermore, its convergence result is given. At last, We report some numerical examples to compare our method with the interior-point method on Maxcut problem.

• Journal of the Korean Mathematical Society
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• v.48 no.5
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• pp.985-999
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• 2011

A new filled function method is presented in this paper to solve box-constrained nonlinear integer programming problems. It is shown that for a given non-global local minimizer, a better local minimizer can be obtained by local search starting from an improved initial point which is obtained by locally solving a box-constrained integer programming problem. Several illustrative numerical examples are reported to show the efficiency of the present method.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.1
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• pp.75-79
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• 1978

The use of three mathematical programming techniques (quadratic programming, integer quadratic programming and linear programming) is discussed to solve some problems in linear regression analysis. When the criterion is the minimization of the sum of squared deviations and the parameters are linearly constrained, the problem may be formulated as quadratic programming problem. For the selection of variables to find "best" regression equation in statistics, the technique of integer quadratic programming is proposed and found to be a very useful tool. When the criterion of fitting a linear regression is the minimization of the sum of absolute deviations from the regression function, the problem may be reduced to a linear programming problem and can be solved reasonably well.ably well.

• Proceeding of KASS Symposium
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• 2008.05a
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• pp.101-106
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• 2008

This paper describes a comparative study of genetic algorithm and mathematical programming technique applied in the design optimization of geodesic dome. In particular, the genetic algorithm adopted in this study uses the so-called re-birthing technique together with the standard GA operations such as fitness, selection, crossover and mutation to accelerate the searching process. The finite difference method is used to calculate the design sensitivity required in mathematical programming techniques and three different techniques such as sequential linear programming (SLP), sequential quadratic programming(SQP) and modified feasible direction method(MFDM) are consistently used in the design optimization of geodesic dome. The optimum member sizes of geodesic dome against several external loads is evaluated by the codes $ISADO-GA{\alpha}$ and ISADO-OPT. From a numerical example, we found that both optimization techniques such as GA and mathematical programming technique are very effective to calculate the optimum member sizes of three dimensional discrete structures and it can provide a very useful information on the existing structural system and it also has a great potential to produce new structural system for large spatial structures.

• IE interfaces
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• v.16 no.4
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• pp.485-495
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• 2003

This paper reports on the application of the integration of mathematical programming model and database in a decision support system (DSS) for the planning of the multi-reservoir operating system. The DSS is based on a multi-objective, mixed-integer goal programming (MIGP) model, which can generate efficient solutions via the weighted-sums method (WSM). The major concern of this study is seamless, efficient integration between the mathematical model and the database, because there are significant differences in structure and content between the data for a mathematical model and the data for a conventional database application. In order to load the external optimization results on the database, we developed a systematic way of naming variable/constraint so that a rapid identification of variables/constraints is possible. An efficient database structure for planning of the multi-reservoir operating system is presented by taking advantage of the naming convention of the variable/constraint.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.74-85
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• 1997

This study aims at the development of a mathematical approach for the optimal water allocation in the river basin where available water is not in sufficient. Its optimal allocation model is determined from the comparison and analysis of mathematical programming techniques such as transportation programming and dynamic programming models at its optimal allocation models. The water allocation system used in this study is designed to be the optimal water allocation which can satisfy the water deficit in each district through inter-basin water transfer between Kumho river basin which is a tributary catchment of Nakdong river basin, and the adjacent Hyungsan river basin, Milyang river basin and Nakdong upstream river basin. A general rule of water allocation is obtained for each district in the basins as the result of analysis of the optimal water allocation in the water allocation system. Also a comparison of the developed models proves that there is no big difference between the models Therefore transportation programming model indicates most adequate to the complex water allocation system in terms of its characteristics It can be seen, however, that dynamic programming model shows water allocation effect which produces greater net benefit more or less.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.336-344
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• 2019

The Ministry of Education (2015) announced the "2015 Revised Curriculum for Elementary and Secondary Schools" and announced that SW (Software) training for elementary and junior high school students to develop Computational Thinking will be gradually introduced from 2018. In addition, 'problem solving' and 'programming' have become important areas. Furthermore, the ability to analyze and utilize big data is becoming more emphasized. We developed and applied the statistical - Python programming convergence curriculum based on the idea that convergence education combining information and mathematics, programming and statistical literacy is needed according to current trends. Before and after the experiment, problem solving ability test and programming / mathematical interest test were conducted and compared with the corresponding sample t-test. According to the analysis results, there were significant differences in the pre- and post-test on problem solving ability, programming interest and mathematical interest at the significance level of 0.05.

• Korean Management Science Review
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• v.12 no.1
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• pp.139-156
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• 1995

This study addresses basic requirements for mathematical programming software, discusses considerations in designing these software, implementation issues facing in these types of applications development, and shows some examples of codes being developed in the course. This type of projects requires long and ever-changing evolutionary phases. The experience is therefore, valuaable in suggesting some useful hints which may be salvaged for similar projects as well as providing reusable codes. In particular, scanning and parsing the free-format inputs, symbol table management, mixed-language programming, and data structures dealing with large sparse matrices are indispensable to many management science software development. Extensions to be made are also discussed.