• Journal of the military operations research society of Korea
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• v.29 no.2
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• pp.61-80
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• 2003

A fire sequencing problem is considered. Fire sequencing problem is a kind of scheduling problem that seeks to minimize the overall time span under a result of weapon­target allocation problem. The assigned weapons should impact a target simultaneously and a weapon cannot transfer the firing against another target before all planned rounds are consumed. The computational complexity of the fire sequencing problem is strongly NP­complete even if the number of weapons is two, so it is difficult to get the optimal solution in a reasonable time by the mathematical programming approach. Therefore, a genetic algorithm is adopted as a solution method, in which the representation of the solution, crossover and mutation strategies are applied on a specific condition. Computational results using randomly generated data are presented. We compared the solutions given by CPLEX and the genetic algorithm. Above $7(weapon){\times}15(target)$ size problems, CPLEX could not solve the problem even if we take enough time to solve the problem since the required memory size increases dramatically as the number of nodes expands. On the other hand, genetic algorithm approach solves all experimental problems very quickly and gives good solution quality.

• Research in Mathematical Education
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• v.13 no.2
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• pp.75-90
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• 2009

There are concepts in calculus which are difficult to teach and learn. One of these concepts is integration. However, problem posing has not yet received the attention it deserves from the mathematics education community. There is no systematic study that deals with teaching of calculus concepts by problem posing oriented teaching strategy. In this respect this study investigated the effect of problem posing on students' (prospective teachers') academic success when problem posing oriented approach is used to teach the integral concept in Calculus-II (Mathematics-II) course to first grade prospective teachers who are enrolled to the Primary Science Teaching Program of Education Faculty. The study used intervention-posttest experimental design. Quantitative research techniques were employed to gather, analyze and interpret the data. The sample comprised 79 elementary prospective science teachers. The results indicate that problem posing approach effects academic success in a positive way and at significant level.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.125-136
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• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.4
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• pp.191-198
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• 2003

The generalized knapsack problem or gknap is the combinatorial optimization problem of optimizing a nonnegative linear function over the integral hull of the intersection of a polynomially separable 0-1 polytope and a knapsack constraint. The knapsack, the restricted shortest path, and the constrained spanning tree problem are a partial list of gknap. More interesting1y, all the problem that are known to have a fully polynomial approximation scheme, or FPTAS are gknap. We establish some necessary and sufficient conditions for a gknap to admit an FPTAS. To do so, we recapture the standard scaling and approximate binary search techniques in the framework of gknap. This also enables us to find a weaker sufficient condition than the strong NP-hardness that a gknap does not have an FPTAS. Finally, we apply the conditions to explore the fully polynomial approximability of the constrained spanning problem whose fully polynomial approximability is still open.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.47-47
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• 1989

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.287-297
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• 2015

A railway security detection system is very important. There are many safety factors that directly affect the safe operation of trains. Security detection technology can be divided into passive and active approaches. In this paper, we will first survey the railway security systems and compare them. We will also propose a subway security detection system with computer vision technology, which can detect three kinds of problems: the spark problem, the obstacle problem, and the lost screw problem. The spark and obstacle detection methods are unique in our system. In our experiment using about 900 input test images, we obtained about a 99.8% performance in F- measure for the spark detection problem, and about 94.7% for the obstacle detection problem.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.157-164
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• 2006

This paper addresses the design of container liner shipping service networks by explicitly taking into account empty container repositioning and container fleet size. Two key and interrelated issues of deployments of ships and containers are usually treated separately by most existing studies on shipping network design. In this paper, both issues are considered simultaneously. The problem is formulated as a two-stage problem: the upper-problem being formulated as a Knapsack problem and the lower-problem as a Flow problem. A genetic algorithm based heuristic is developed for the problem. Through a number of numerical experiments that were conducted it was shown that the problem considering empty container repositioning provides a more insightful solution than the one without.

• Education of Primary School Mathematics
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• v.4 no.1
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• pp.51-61
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• 2000

Mathematical Problem solving has been the focus of a considerable amount of research over past 30 years. But nowadays problem solving is being beginning to be of less interest to mathematics education researchers. Moreover, mathematics teachers have an urgent need to be provided with well-documented informations about "teaching of(expecially, via) problem solving" though following research issues ：ⅰ) the role of the teacher in a problem-centered classroom, ⅱ) what actually takes place in problem-centered classrooms, and iii) groups and whole classes' problem solving rather than individuals. This paper intends to give some informations about practice of teaching mathematical problem solving in elementary school.ry school.

• Educational Technology International
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• v.14 no.2
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• pp.137-166
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• 2013

Problem-based learning (PBL) in engineering education has been implemented in various ways. The wide range of PBL methods sometimes creates difficulties in implementing PBL. The purpose of this study was to identify the major variables that a teacher considers in PBL designs for an engineering course and suggest specific PBL methods according to the PBL design variables. This study was conducted using a review research method involving 21 studies from a range of engineering education fields. The results showed that the major variables that engineering professors need to consider when applying PBL are the authenticity of the PBL problem and the method of providing knowledge or information that the learners must know to solve the given problem. Based on the two variables identified, the following four types of PBL methods for engineering education are suggested: 1) lecture-based problem, 2) guided problem-based learning, 3) problem-based learning and 4) co-op problem-based learning.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.2
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• pp.36-41
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• 1987

This paper presents a heuristic algorithm for solving the simple facility location problem. Its main procedure is essentially of 'add' type, which progressively selects facilities to open according to a certain criterion derived from the analysis of the linear programming dual. Computational experience with test problem from the literature is presented.