• 대한산업공학회지
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• 제23권4호
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• pp.645-660
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• 1997

An application of the genetic algorithm(GA) to the loading sequencing problem in port container terminals is presented in this paper. The efficiency of loading operations in port container terminals is highly dependent on the loading sequence of export containers. In order to sequence the loading operation, we hove to determine the route of each container handling equipment (transfer crane or straddle carried in the yard during the loading operation. The route of a container handling equipment is determined in a way of minimizing the total container handling time. An encoding method is developed which keeps intermediate solutions feasible and speeds up the evolution process. We determine the sequence of each individual container which the container handling equipment picks up at each yard-bay as well as the visiting sequence of yard-bays of the equipment during the loading operation. A numerical experiment is carried out to evaluate the performance of the algorithm developed.

• Industrial Engineering and Management Systems
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• 제3권1호
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• pp.22-37
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• 2004

An integrated heuristic approach based on genetic algorithms (GAs) is proposed for solving the container selection and loading problems. The GA for container selection solves a two-dimensional knapsack problem, determining a set of containers to minimize the transportation or shipment cost. The GA for container loading solves for the weighted coefficients in the evaluation functions that are applied in selecting loading positions and boxes to be loaded, so that the volume utilization is maximized. Several loading constraints such as box orientation, stack priority, stack stability, and container stability are also incorporated into the algorithm. In general, our computational results based on randomly generated data and problems from the literature suggest that the proposed heuristic provides a good solution in a reasonable amount of computational time.

• 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.

• Journal of Biosystems Engineering
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• 제20권1호
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• pp.87-94
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• 1995

When farmer loads and unloads farm products with a trailer, linked to a tractor, the tractor-trailer is backed up to the loading duck. However, travelling backward is not easy and takes a time for even skilled operators. Therefore, unmanned backing up is necessary to save the effort. A backward controller of tractor-trailer was simulated using fuzzy logic and genetic algorithms. Operators drive the tractor-trailer back and forth several times for backing up to the loading duck. As the operators did it, a backward controller was designed using fuzzy logic. And genetic algorithms was applied to improve the performance of the backward controller. With the strings coded with the fuzzy membership functions, genetic operations were carried out. After 30 generations, the best fitted fuzzy membership functions were found. Those membership functions were used in the fuzzy backward controller. The fuzzy controller combined with genetic algorithms showed the better results than the fuzzy controller did alone.

• 산업경영시스템학회지
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• 제29권4호
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• pp.75-82
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• 2006

The main objective of this study is to find out the shortest path of the vehicle routing problem with time window constraints by using both genetic algorithm and heuristic. Hard time constraints were considered to the vehicle routing problem in this suggested algorithm. Four different heuristic rules, modification process for initial and infeasible solution, 2-opt process, and lag exchange process, were applied to the genetic algorithm in order to both minimize the total distance and improve the loading rate at the same time. This genetic algorithm is compared with the results of existing problems suggested by Solomon. We found better solutions concerning vehicle loading rate and number of vehicles in R-type Solomon's examples R103 and R106.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• 대한교통학회지
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• 제21권1호
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• pp.65-75
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• 2003

교통혼잡 문제가 점점 심각해짐에 따라 대중교통의 중요성은 날로 부각되며, 대중교통을 지원하기 위한 정책들이 속속 입안되고 있어 대중교통을 심도 있게 분석할 수 있는 틀의 개발은 필연적이라 할 수 있다. 이에 본 연구는 대중교통의 특성을 고려하는 대중교통 통행배정모형의 개발을 목적으로 수행되었다. 본 연구에서 개발한 대중교통 통행배정모형은 기존의 대중교통 통행배정모형이 개별차량과 다른 대중교통의 특성을 정확히 반영하고 있지 못하다는 한계를 극복하고자, 최적경로 탐색에는 유전자 알고리즘(Genetic Algorithm)을 통행량 배정에는 로짓모형을 기반으로 한 확률적 통행량 배정모형(Stochastic Network Loading Model)을 이용하였다.

• Journal of Mechanical Science and Technology
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• 제20권10호
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• pp.1541-1547
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• 2006

An efficient procedure to obtain the optimal stacking sequence and the minimum weight of stiffened laminated composite curved panels under several loading conditions and stiffener layouts has been developed based on the finite element method and the genetic algorithm that is powerful for the problem with integer variables. Often, designing composite laminates ends up with a stacking sequence optimization that may be formulated as an integer programming problem. This procedure is applied for a problem to find the stacking sequence having a maximum critical buckling load factor and the minimum weight. The object function in this case is the weight of a stiffened laminated composite shell. Three different types of stiffener layouts with different loading conditions are investigated to see how these parameters influence on the stacking sequence optimization of the panel and the stiffeners. It is noticed from the results that the optimal stacking sequence and lay-up angles vary depending on the types. of loading and stiffener spacing.

• 생물정신의학
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• 제15권2호
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• pp.83-91
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• 2008

목 적 연성 신경학적 징후는 정신분열병의 유전과 관계되는 생물학적 표현형으로 여겨져 왔다. 본 연구에서는 연성 신경학적 징후가 유전적 하중과 관련해서 가족 내에서 연관성을 보이는지를 알아보고자 하였다. 대상 및 방법 정신분열병 환자로서 부모 중 한 명은 정신분열병의 가족력이 있고 나머지 한 명은 가족력이 없는 환자가(N=14) 모집되었다. 이들의 부모가 가진 가족력에 따라 유전적 하중을 정하였고 이를 위해 Family Interview for Genetic Studies(FIGS)로 직접 면담을 시행하였다. 환자의 부모에서 환자를 제외한 1차 혹은 2차 친척에서 정신분열병 환자가 있는 부모를 유전적 하중이 있는 '추정 보유자'(presumed carrier)(N=9)로, 그렇지 않은 부모를 '추정 비보유자' (presumed noncarrier)(N=11)로 구분하였다. 또한 정상 대조군은 1차 혹은 2차 친척에서 정신분열병의 가족력이 없는 일반인(N=12)이었다. 이들의 연성 신경학적 징후를 한국어판 신경학적 평가 척도(Neurological Evaluation Scale-Korean version)를 사용하여 평가하였으며 가족내 연관성에 대한 분석 방법으로 급내 상관계수법(Intra-Class Coefficients[ICC]) method)을 이용하였다. 결 과 연성 신경학적 징후의 하위 영역 중 운동 조정 영역에서 환자와 추정 보유자 간에 유의한 연관성이 존재하였고(ICC=.804, p=.016) 환자와 추정 비보유자와는 연관성이 존재하지 않았다. 운동 조정 영역 이외의 다른 하위 영역에서는 유전적 하중과 상관없이 가족내 연관성이 존재하지 않았다. 그러나, 환자군과 정상 대조군의 운동 조정 영역의 점수 간에는 통계적으로 유의한 차이가 존재하지 않았다. 결 론 이번 연구에서는 연성 신경학적 징후들이 정신분열병의 유전적 하중과 같이 유전되는 생물학적 표현형일 가능성이 입증되지 못하였다. 향후 보다 많은 수를 대상으로 한 추시 연구가 필요하다.

• 한국정보통신학회논문지
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• 제24권2호
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• pp.186-191
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• 2020

이 논문에서는 차량 운반선에서 선적 계획과 하적 계획에 따른 차량 선적을 효과적으로 진행하기 위하여, 선박 내 데크에 차량을 효율적으로 배치하는 최적화 기법을 제안하였다. 이를 위해, 선박의 공간 정보를 나타내는 XML 데이터의 변환, 병합 및 분할 알고리즘, 유전자 알고리즘을 활용하였으며, 또한 최적화된 차량 배치 결과를 시각화하는 기능까지 구현하였다. 기존의 전형적인 유전자 알고리즘에서 사용되는 선택, 교차, 변이, 엘리트 보존 등의 기법들을 활용하였으며, 특히 차량의 선적을 위한 선박 공간을 병합 및 분할하는 기법을 함께 제안하여 차량 배치 최적화 기법을 제안하였다. 실험 결과, 기존의 유전자 알고리즘만으로 최적화하기 힘든 부분에 제안한 병합 및 분할 기법을 적용하는 것이 최적화 과정에 효과적이었음을 확인할 수 있었다. 또한, 시각화 기법을 통해 차량 배치 결과를 도면 형태로 보여줌으로써 배치 결과의 효율성을 전문가가 쉽게 판단할 수 있도록 하였다.