• 한국철도학회논문집
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• 제9권2호
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• pp.206-211
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• 2006

This thesis presents railroad crew restoring problem, which is to determine the railroad plan allocation. This problem is constructed that determine the sequence of duties that railroad crews have to perform. We analyze characteristic of this problem and railroad industry. It's hard to consider many constraint conditions. We propose Integer Programming model and easy methodology to be considered all given operation rules. This problem is known to be NP-hard. We develop a genetic algorithm, which is proved to be powerful in solving optimization problems. We proposed the effective mathematical model and algorithm about making crew restoring in real industry.

• Industrial Engineering and Management Systems
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• 제4권2호
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• pp.136-144
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• 2005

This research deals with an order picking problem in automated storage and retrieval systems (AS/RS). When retrieval requests consist of multiple items and the items are in multiple stock locations, the storage/retrieval (S/R) machine must travel to numerous storage locations to complete each order. The aim of this research is to propose algorithms for the resolution of order picking problems with multiple stock locations to minimize the total time traveled by the S/R machine. We present and compare three alternatives for solving the problem based on enumeration, ordinary heuristic and genetic algorithms. We used a set of 180 different problems that are solved by these three algorithms. The results show that our proposed genetic algorithm is more efficient than the other two.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.252-256
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• 1997

The success of a fuzzy neural network(FNN) control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. A messy genetic algorithm is used to obtain structurally optimized FNN models. Structural optimization is regarded important before neural networks based learning is switched into. We have applied the method to the problem of a numerical approximation

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.490-498
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• 2013

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

• 한국경영과학회지
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• 제25권4호
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• pp.97-109
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• 2000

This work proposes a neural network approach to solve vehicle routing problems which have diverse application areas such as vehicle routing and robot programming. In solving these problems, classical mathematical approaches have many difficulties. In particular, it is almost impossible to implement a real-time vehicle routing with multiple vehicles. Recently, many researchers proposed methods to overcome the limitation by adopting heuristic algorithms, genetic algorithms, neural network techniques and others. The most basic model for path planning is the Travelling Salesman Problem(TSP) for a minimum distance path. We extend this for a problem with dynamic upcoming of new positions with multiple vehicles. In this paper, we propose an algorithm based on SOM(Self-Organization Map) to obtain a sub-optimal solution for a real-time vehicle routing problem. We develope a model of a generalized multiple TSP and suggest and efficient solving procedure.

• International Journal of Precision Engineering and Manufacturing
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• 제1권1호
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• pp.62-70
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• 2000

The design of gear train is a kind of mixed problems which have to determine various types of design variables; i,e., continuous, discrete, and integer variables. Therefore, the most common practice of optimum design using the derivative of objective function has difficulty in solving those kinds of problems and the optimum solution also depends on initial guess because there are many sophisticated constrains. In this study, the Genetic Algorithm is introduced for the optimum design of gear trains to solve such problems and we propose a genetic algorithm based gear design system. This system is applied for the geometrical volume(size) minimization problem of the two-stage gear train and the simple planetary gear train to show that genetic algorithm is better than the conventional algorithm solving the problems that have continuous, discrete, and integer variables. In this system, each design factor such as strength, durability, interference, contact ratio, etc. is considered on the basis of AGMA standards to satisfy the required design specification and the performance with minimizing the geometrical volume(size) of gear trains

• Journal of Ship and Ocean Technology
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• 제9권4호
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• pp.35-46
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• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• 한국과학교육학회지
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• 제40권4호
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• pp.385-398
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• 2020

본 연구에서는 서로 다른 학습접근방식의 학생이 유전 가계도 문제 해결 과정상에서 어떠한 차이를 보여주는지 심층적으로 들여다보고자 하였다. 연구 대상은 고등학교 2학년 학생으로 생명과학I을 이수한 학생으로 학업성취수준은 비슷하였으나 학습접근방식이 각각 심층적 접근방식과 피상적 접근방식을 나타내었다. 각 학생의 문제 해결 사례를 심층적으로 분석하기 위해 문제 해결 과정은 비디오 녹화되었고, 문제 해결이 종료된 후에 학생들의 문제 해결 과정에 대한 사고 구술 인터뷰를 실시하였다. 연구 결과, 학생들은 2가지 형질의 유전 원리가 불확실한 문제 상황을 해결하는 과정에서 유사한 오류를 보여주었다. 하지만 심층적 학습접근방식의 학생 A는 자발적으로 2번 반복하여 문제를 해결하면서 3가지 제한 요인에 대해 원리 기반추론을 하며 옳은 개념적 프레이밍을 나타내었다. 검토 단계에서 자료와 본인이 그린 가계도 사이의 일치도를 점검하고, 문제 해결 이후에도 끊임없이 본인의 문제 해결 과정을 점검하였다. 마지막의 문제 해결 과정에서는 성공적인 문제 해결 알고리즘에 근접한 문제 해결 과정을 나타내었다. 하지만 피상적 학습접근방식의 학생 B는 연구자의 권유로 비자발적으로 문제 해결 과정을 반복하였고, 답을 구하려는 목적 지향적인 문제 해결 태도로 인해 문제에서 제시한 일부 정보만을 검토하였다. 문제의 제한 요인에 대해 기억 장치 추론이나 임의적 추론을 통해서 옳지 않은 개념적 프레이밍을 하였고, 이를 수정하지 않고 유지하는 모습을 나타내었다. 본 연구 결과를 통해 심층적 접근방식과 피상적 접근방식의 학생이 문제 해결 과정에서 추론 방식과 개념적 프레이밍의 변화가 어떻게 일어나는지 구체적으로 살펴봄으로써 유전 문제의 접근을 어려워하는 학생들이나 이들을 지도하는 교사들에게 도움을 줄 수 있을 것이다.

• Industrial Engineering and Management Systems
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• 제12권3호
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• pp.181-189
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• 2013

This paper proposes a two-phase genetic algorithm (GA) to solve the problem of obtaining an optimum configuration of a paired single row assembly line. We pair two single-row assembly lines due to the shared usage of several workstations, thus obtaining an optimum configuration by considering the material flow of the two rows simultaneously. The problem deals with assigning workstations to a sequence and selecting the best arrangement by looking at the length and width for each workstation. This can be considered as an enhancement of the single row facility layout problem (SRFLP), or the so-called paired SRFLP (PSRFLP). The objective of this PSRFLP is to find an optimal configuration that seeks to minimize the distance traveled by the material handler and even the use of the material handler itself if this is possible. Real-world applications of such a problem can be found for apparel, shoe, and other manual assembly lines. This research produces the schematic representation solution using the heuristic approach. The crossover and mutation will be utilized using the schematic representation solution to obtain the neighborhood solutions. The first phase of the GA result is recorded to get the best pair. Based on these best matched pairs, the second-phase GA can commence.

• 예술인문사회 융합 멀티미디어 논문지
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• 제8권4호
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• pp.259-267
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• 2018

부분집합 합 문제는 유한개의 정수로 이루어진 집합이 있을 때 이 집합의 부분집합 중에서 그 집합의 원소들의 합이 특정 값이 되는 경우가 있는지를 알아내는 문제로, 잘 알려진 다항식 시간 내에 풀기 어려운 NP-완비 문제이다. 유전 알고리즘은 선택과 교차, 돌연변이 등의 연산을 통해 주어진 문제의 최적해를 구하는 알고리즘이다. 동적 계획법은 주어진 문제를 풀기 위해서 문제를 하나 또는 여러 개의 하위 문제로 나누어 풀이하는 방법이다. 본 논문에서는 부분집합 합 문제를 풀이하는 유전 알고리즘을 설계 및 구현하고, 답을 찾는 데까지 걸리는 시간 성능을 동적 계획법의 경우와 실험적으로 비교하였다. 양의 정수인 원소 63 개를 가진 집합에서 '쉬움'과 '어려움'의 난이도를 고려하여 총 17 개의 문제를 선정하고, 이 문제들을 풀이하는 두 알고리즘의 성능을 비교하는 실험을 진행하였다. 17 개의 문제 중 13 개의 문제에서 본 논문에서 제시한 유전 알고리즘은 동적 계획법과 비교하여 약 84%가 우수한 시간 성능을 보였다.