• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.8
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• pp.907-914
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• 2014

A heuristic search algorithm is proposed to plan a collision free path for robots in an indoor environment. The proposed algorithm is to find a collision free path in the gridded configuration space by proposed heuristic graph search algorithm. The proposed algorithm largely consists of two parts : tunnel searching and path searching in the tunnel. The tunnel searching algorithm finds a thicker path from start grid to goal grid in grid configuration space. The tunnel is constructed with large grid defined as a connected several minimum size grids in grid-based configuration space. The path searching algorithm then searches a path in the tunnel with minimum grids. The computational time of the proposed algorithm is less than the other graph search algorithm and we analysis the time complexity. To show the validity of the proposed algorithm, some numerical examples are illustrated for robot.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.841-848
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• 2004

Evolutionary a1gorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uni-formly distributed onto Pareto optimal front. In this paper, we propose an evolutionary a1gorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms Published. Seed individual means a solution which is not located in the crowded region on Pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed a1go-rithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algo-rithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-modal problem.

• The Journal of Society for e-Business Studies
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• v.14 no.1
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• pp.143-160
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• 2009

In determining the performance of OLAP database applications, the structure and the effective access methods to the underlying disk system is a significant factor. In recent years, hard disks are designed with multiple physical zones where seek times and data transfer rates vary across the zones. However, there is little consideration of multi-zone disks in previous works. Instead, they assumed a traditional disk model that comes with many simplifying assumptions such as an average seek-time and a single data transfer rate. In this paper, we propose a technique storing a set of materialized views into the multi-zoned disks in OLAP environment dealing with large sets of data. We first present the disk zoning algorithm of materialized views according to the access probabilities of each views. Also, we address the problem of storing views in the dynamic environment where data are updated continuously. Finally, through experiments, we prove the performance improvement of the proposed algorithm against the conventional methods.

• The Journal of Korean Association of Computer Education
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• v.16 no.5
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• pp.9-16
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• 2013

Computational thinking is an ability to resolve problems that may be applied to the various real world problems and is regarded as the core of computer science. Computational thinking may be improved through experiences of analyzing problems and of selecting, applying, and modeling strategies appropriate for problem-solving. In order to enhance computational thinking of learners, it is important to provide experiences of solving various problems. This study designed puzzle based learning in order to educate learners principles of problem solving, let them have experiences of interest and insight, and provide them with problem solving experiences. The puzzle questions used for learning were classified into six types - constraints, optimization, probability, statistics, pattern recognition, and strategies. These questions were applied to Informatics gifted elementary students and, after their education, their computational thinking and problem solving inventory significantly improved.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.70-81
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• 1998

This paper proposes an accurate and cost-effective COA defuzzifier of fuzzy logic controller (FLC). The accuracy of the proposed COA defuzzifier is obtained by involving both membership values and spans of membership functions in calculating a crisp value. The cost-effectiveness of the proposed COA defuzzifier is obtained by replacing the division in the COA defuzzifier by finding an equilibrium point of both the left and right moments. The proposed COA defuzzifier has two disadvantages that it ncreases the hardware complexity due to the additional multipliers and it takes a lot of computation time to find the moment equilibrium point. The first disadvantage is overcome by replacing the multipliers with the stochastic AND operations. The second disadvantage is alleviated by using a coarse-to-fine searching algorithm that accelerates the finding of moment equilibrium point. Application of the proposed COA defuzzifier to the truck backer-upper control problem is performed in the VHDL simulation and the control accuracy of the proposed COA defuzzifier is compared with that of the conventional COA defuzzifier in terms of average tracing distance.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.11
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• pp.75-83
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• 1999

We propose a PfSGA(parameter-free species genetic algorithm) to learn the topological structure of MLP classifiers being adequate to given applications. The PfSGA is a combinational method of SGA(species genetic algorithm) and PfGA(parameter-free genetic algorithm). In SGA, we divide the total search space into several subspaces(species) according to the number of hidden units, and reduce the unnecessary search by eliminating the low promising species from the evolutionary process. However the performances of SGA classifiers are readily affected by the values of parameters such as mutation ratio and crossover ratio. In this paper, therefore, we combine SGA with PfGA, for which it is not necessary to determine the learning parameters. Experimental results on benchmark data and sign language words show that PfSGA can reduce the learning time of SGA and is not affected by the selection parameter values on structural learning. The results also show that PfSGA is more efficient than the exisiting methods in the aspect of misclassification ratio, learning rate, and complexity of MLP structure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.692-702
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• 2001

In this paper, a new block-matching algorithm for standard video encoder is proposed. The algorithm finds a motion vector using the increasing SAD transition curve for each predefined candidates, not a coarse-to-fine approach as a conventional method. To remove low-probability candidates at the early stage of accumulation, a dispersed accumulation matrix is also proposed. This matrix guarantees high-linearity to the SAD transition curve. Therefore, base on this method, we present a new fast block-matching algorithm with the slice competition technique. The Candidate Selection Step and the Candidate Competition Step makes an out-performance model that considerably reduces computational power and not to be trapped into local minima. The computational power is reduced by 10%~70% than that of the conventional BMAs. Regarding computational time, an 18%~35% reduction was achieved by the proposed algorithm. Finally, the average MAD is always low in various bit-streams. The results were also very similar to the MAD of the full search block-matching algorithm.

• Journal of the Korean School Mathematics Society
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• v.14 no.3
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• pp.295-323
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• 2011

This study is based on the recognition that teacher educators have to focus their attention on developing pre-service teachers' statistical reasoning for statistics education of school mathematics. This paper investigated knowledge on pre-service teachers' statistical reasoning. Statistical Reasoning Assessment (SRA) is performed to find out pre-service teachers' statistical reasoning ability. The research findings are as follows. There was meaningful difference in the statistical area of statistical reasoning ability with significant level of 0.05. This proved that 4 grades pre-service teachers were more improve on statistical reasoning than 2 grades pre-service teachers. Even though most of the pre-service teachers ratiocinated properly on SRA, half of pre-service teachers appreciated that small size of sample is more likely to deviate from the population than the large size of sample. A few pre-service teachers have difficulties in understanding "Correctly interprets probabilities(be able to explain probability by using ratio" and "Understands the importance of large samples(A small sample is more likely to deviate from the population)".

• Science of Emotion and Sensibility
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• v.17 no.2
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• pp.63-76
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• 2014

Although most behavioral reaction times (RTs) for cognitive tasks exhibit positively skewed distributions, the majority of studies primarily rely on a measure of central tendency (e.g. mean) which can cause misinterpretations of data's underlying property. The purpose of current study is to introduce procedures for describing characteristics of RT distributions, thereby effectively examine the influence of experimental manipulations. On the basis of assumption that RT distribution can be represented as a convolution of Gaussian and exponential variables, we fitted the ex-Gaussian function under a maximum-likelihood method. The ex-Gaussian function provides quantitative parameters of distributional properties and the probability density functions. Here we exemplified distributional analysis by using empirical RT data from two conventional visual search tasks, and attempted theoretical interpretation for setsize effect leading proportional mean RT delays. We believe that distributional RT analysis with a mathematical function beyond the central tendency estimates could provide insights into various theoretical and individual difference studies.