• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.569-572
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• 2000

DEA has been effectively applied to various areas which need the evaluation of relative efficiency. We propose a DEA model based on fuzzy LP combined with Genetic Algorithm in order to consider uncertain synergy effects due to M&A of existing organization. We apply the suggested approach to forecasting the efficiency of merged academic departments in a university in Korea. We expect that our approach can be utilized to effectively realign existing departments.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.79-89
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• 2017

The Optimization of a Closed-Loop Supply Chain (CLSC) Model Using an Adaptive Hybrid Genetic Algorithm (AHGA) Approach is Considered in this Paper. With Forward and Reverse Logistics as an Integrated Logistics Concept, The CLSC Model is Consisted of Various Facilities Such as Part Supplier, Product Manufacturer, Collection Center, Recovery Center, etc. A Mathematical Model and the AHGA Approach are Used for Representing and Implementing the CLSC Model, Respectively. Several Conventional Approaches Including the AHGA Approach are Used for Comparing their Performances in Numerical Experiment.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.946-949
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• 2005

It is very a time-consuming and error-prone process to obtain the optimal injection condition, which can produce good injection molding products in some operational variation of facilities, from a seed injection condition. This study proposes a new approach to search the optimal injection molding condition using a neural network and a genetic algorithm. To estimate the defect type of unknown injection conditions, this study forces the neural network into learning iteratively from the injection molding conditions collected. Major two parameters of the injection molding condition - injection pressure and velocity are encoded in a binary value to apply to the genetic algorithm. The optimal injection condition is obtained through the selection, cross-over, and mutation process of the genetic algorithm. Finally, this study compares the optimal injection condition searched using the proposed approach. with the other ones obtained by heuristic algorithms and design of experiment technique. The comparison result shows the usability of the approach proposed.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.23 no.2
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• pp.1-7
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• 2023

Inherited metabolic disorders (IMD) are a group of disorders involving various metabolic pathways. Genetic diagnosis of IMD has been challenging because of extremely heterogeneous nature and extensive laboratory and/or phenotype overlap. Conventional genetic diagnosis was a gene-by-gene approach that needs a priori information on the causative genes that might underlie the IMD. Recent implementation of next-generation sequencing (NGS) technologies has changed the process of genetic diagnosis from a gene-by-gene approach to simultaneous analysis of targeted genes possibly associated with the IMD using gene panels or using whole exome/genome sequencing (WES/WGS) covering entire human genes. Clinical NGS tests can be a cost-effective approach for the rapid diagnosis of IMD with genetic heterogeneity and are becoming standard diagnostic procedures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.159-169
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• 2020

A cold chain logistics (CCL) model for chilled food (-1℃ to 8℃) distributed in China was developed in this study. The CCL model consists of a distribution center (DC) and distribution target points (DT). The objective function of the CCL model is to minimize the total distribution routes of all distributors. To find the optimal result of the objective function, the hybrid genetic algorithm (HGA) approach is proposed. The HGA approach was constructed by combining the improved K-means and genetic algorithm (GA) approaches. In the case study, three scenarios were considered for the CCL model based on the distribution routes and the available distance, and they were solved using the proposed HGA approach. Analysis results showed that the distribution costs and mileage were reduced by approximately 19%, 20% and 16% when the proposed HGA approach was used.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.125-139
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• 2004

Generally, parallel redundancy is used to improve reliability in many systems. However, redundancy increases system cost, weight, volume, power, etc. Due to limited availability of these resources, the system designer has to maximize reliability subject to various constraints or minimize resources while satisfying the minimum requirement of system reliability. This paper presents GAs (Genetic Algorithms) to solve redundancy allocation in series-parallel systems. To apply the GAs to this problem, we propose a genetic representation, the method for initial population construction, evaluation and genetic operators. Especially, to improve the performance of GAs, we develop heuristic operators (heuristic crossover, heuristic mutation) using the reliability-resource information of the chromosome. Experiments are carried out to evaluate the performance of the proposed algorithm. The performance comparison between the proposed algorithm and a pervious method shows that our approach is more efficient.

• IE interfaces
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• v.19 no.4
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• pp.300-305
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• 2006

The problem of assigning customers to satellite channels is considered in this paper. Finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is shown to be NP-complete in an earlier study. We propose a genetic algorithm (GA) approach to search for the best/optimal assignment of customers to satellite channels. Various issues related to genetic algorithms such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. A comparison of GA with CPLEX8.1 is presented to show the advantages of this approach in terms of computation time and solution quality.

• School Mathematics
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• v.5 no.4
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• pp.401-420
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• 2003

We have many problems in the teaching and learning of proof, especially in the demonstrative geometry of middle school mathematics introducing the proof for the first time. Above all, it is the serious problem that many students do not understand the meaning of proof. In this paper we intend to show that teaching the meaning of proof in terms of historic-genetic approach will be a method to improve the way of teaching proof. We investigate the development of proof which goes through three stages such as experimental, intuitional, and scientific stage as well as the development of geometry up to the completion of Euclid's Elements as Bran-ford set out, and analyze the teaching process for the purpose of looking for the way of improving the way of teaching proof through the historic-genetic approach. We conducted lessons about the angle-sum property of triangle in accordance with these three stages to the students of seventh grade. We show that the students will understand the meaning of proof meaningfully and properly through the historic-genetic approach.