• Industrial Engineering and Management Systems
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• 제11권4호
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• 해양환경안전학회지
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• 제6권1호
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• pp.23-33
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• 2000

This paper is concerned with finding the global optimal solutions for the redundancy optimization problems in series-parallel systems related with system safety. This study transforms the difficult problem, which is classified as a nonlinear integer problem, into a 0/1 IP(Integer Programming) by using binary integer variables. And the global optimal solution to this problem can be easily obtained by applying GAMS (General Algebraic Modeling System) to the transformed 0/1 IP. From computational results, we notice that GA(Genetic Algorithm) to this problem, which is, to our knowledge, known as a best algorithm, is poor in many cases.

• 대한교통학회지
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• 제17권2호
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• pp.163-178
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• 1999

교통분야에서 이용되는 최단경로 알고리듬은 분할탐색 기법에 기초를 두고 있다. 분할탐색 기법이란 기점으로부터 일정 영역을 분할하여 경로를 탐색, 종점가지의 경로를 구축하는 방법으로써 수형망(Tree Building)알고리듬이나 덩굴망(Vine Building) 알고리듬 등이 여기에 속한다. 그러나 이러한 분할탐색기법의 경우 교통망내에서 복수 수단간의 환승비용이 고려될 경우나 동적 최단경로를 탐색하는 경우에는 교통망을 확장하지 않으면 기종점간의 올바른 최단경로를 찾을 수 없다는 문제점을 가지고 있다. 이러한 문제를 본 연구에서는 탐색 영역 문제(Searching Area Problem)라고 정의하였다. 본 연구에서는 탐색영역문제를 교통망 확장없이 해결할 수 있는 전역 탐색기법으로 유전 알고리듬을 이용하여 개발하였다.

• 한국통신학회논문지
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• 제32권11A호
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• pp.1096-1105
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• 2007

본 논문에서는 MB-OFDM UWB 시스템의 간섭 회피 기술로서 무선 인지 기술(Cognitive Radio)을 사용하였다. 간섭 신호를 측정하기 위한 방안으로 FCC에서 제안한 무선 인지 기술의 간섭 온도 모델(Interference Temperature Model) 사용한다. 간섭 온도 측정을 통하여 MB-OFDM UWB 시스템의 채널 용량(Channel Capacity)를 계산한 후 간섭 상황을 해결하는 방안을 제시한다. 계산 과정에 해당하는 인지 엔진(Cognitive Engine)의 연산 알고리즘으로 사용될 유전 알고리즘을 사용하였다. 본 논문에서 제안한 국내 환경에 적합한 무선 인지 기술(Cognitive Radio using Interference Temperature Model Access)을 이용한 MB-OFDM UWB 시스템은 현재 문제가 될 수 있는 UWB 통신 시스템의 간섭문제를 해결하는데 좋은 성능을 보여주고 있는 것을 확인하였다.

• Smart Structures and Systems
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• 제16권1호
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• Advances in Computational Design
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• 제7권1호
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• pp.1-17
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• 2022

In this study, a new strategy is presented to transmit the fundamental elastic beam problem into the modern optimization platform and solve it by using artificial intelligence (AI) tools. As a practical example, deflection of Euler-Bernoulli beam is mathematically formulated by 2nd-order ordinary differential equations (ODEs) in accordance to the classical beam theory. This fundamental engineer problem is then transmitted from classic formulation to its artificial-intelligence presentation where the behavior of the beam is simulated by using neural networks (NNs). The supervised training strategy is employed in the developed NNs implemented in the heuristic optimization algorithms as the fitness function. Different evolutionary optimization tools such as genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve this non-linear optimization problem. The step-by-step procedure of the proposed method is presented in the form of a practical flowchart. The results indicate that the proposed method of using AI toolsin solving beam ODEs can efficiently lead to accurate solutions with low computational costs, and should prove useful to solve more complex practical applications.

• 정보처리학회논문지B
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• 제15B권2호
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• pp.131-136
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• 2008

그래프 G = (V, E) 의 L(2,1)-labeling 이란 함수 f: V(G) $\rightarrow$ {0, 1, 2, ...} 를 정의하는 것으로서 함수 f 는 만일 G 내의 두 개 정점 u, $\upsilon$ 사이의 최단거리가 1 인 경우 $|f(u)\;-\;f(\upsilon)|\;{\geq}\;2$ 라는 조건 및 최단거리가 2 인 경우 $|f(u)\;-\;f(\upsilon)|\;{\geq}\;1$ 라는 조건을 만족시켜야 한다. ${\lambda}(G)$ 로 표기되는 G 의 L(2,1)-labeling 수는 모든 가능한 f 들 사이에서 사용된 가장 큰 정수가 가장 작은 값을 나타낸다. 상기한 문제는 NP-complete 계열의 문제이기 때문에 본 논문에서는 L(2,1)-labeling 에 적용 가능한 유전자 알고리즘을 개발한 후 개발된 알고리즘을 최적값이 알려진 그래프들에 적용하여 그 효율성을 보이고자 한다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2003년도 추계학술대회 및 정기총회
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• pp.259-262
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• 2003

Group technology(GT) is a manufacturing philosophy which identifies and exploits the similarity of parts and processes in design and manufacturing. A specific application of GT is cellular manufacturing. the first step in the preliminary stage of cellular manufacturing system design is cell formation, generally known as a machine-part cell formation(MPCF). This paper presents and tests a grouping gentic algorithm(GGA) for solving the MPCF problem and uses the measurements of e(ficacy. GGA's replacement heuristic used similarity coefficients is presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.127-129
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• 2002

This paper presents a fuzzy-GA method to resolve dispersed generator placement for distribution systems. The problem formulation considers an objective to reduce power loss costs of distribution systems and the constraints with the number or size of dispersed generators and the deviation of the bus voltage. The main idea of solving fuzzy nonlinear goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature and solve the problem using the proposed genetic algorithm, without any transformation for this nonlinear problem to a linear model or other methods. The method proposed is applied to the sample systems to demonstrate its effectiveness.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.844-846
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• 1998

In this study, an adaptive evolutionary computation(AEC), which uses adaptively a genetic algorithm having global searching capability and an evolution strategy having local searching capability with different methodologies, is suggested. This paper develops AEC for solving ELD problem with piecewise quadratic cost function. Numerical results show that the proposed AEC can provide accurate dispatch solutions within reasonable time for the ELD problem with piecewise quadratic cost function.