• 산업경영시스템학회지
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• 제37권4호
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• pp.187-192
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• 2014

Lot-streaming is the process of splitting a job (lot) into sublots to allow the overlapping of operations between successive machines in a multi-stage production system. A new genetic algorithm (NGA) is proposed for an n-job, m-machine, lot-streaming flow shop scheduling problem with equal-size sublots in which the objective is to minimize the total stretch. The stretch of a job is the ratio of the amount of time the job spent before its completion to its processing time. NGA replaces the selection and mating operators of genetic algorithms (GAs) by marriage and pregnancy operators and incorporates the idea of inter-chromosomal dominance and individuals' similarities. Extensive computational experiments for medium to large-scale lot-streaming flow-shop scheduling problems have been conducted to compare the performance of NGA with that of GA.

• Journal of Forest and Environmental Science
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• 제25권3호
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• pp.147-155
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• 2009

Higher plants provide valuable genetic assay systems for screening and monitoring environmental pollutants. They are now recognized as excellent indicators of mutagenic effects of environmental chemicals and are applicable for the detection of environmental mutagens both indoor and outdoor. 2,4-dichlorophenoxyacetic acid (2,4-D) is a herbicide commonly used in agriculture. The residues of 2,4-D are present in air, water, soil and edible plants. It constitutes a real hazard to the public health because it's wide spread use in agriculture. Genotoxic effects of 2,4-D on plant cells and potential of higher plants as a biomonitoring system for detecting chemical mutagens are evaluated. It is recommended that higher plant systems have been accepted by regulatory authorities as an alternative biomonitoring system for the detection of possible genetic damage resulting from pollution and the use of environmental chemicals.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2002년도 춘계학술대회 논문집
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• pp.106-111
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• 2002

해상운송에서 규모의 경제 이익을 달성하기 위해 선박의 크기가 대형화되고, 항만이 중심항만과 주변항만으로 양분화 되어감에 따라 컨테이너 터미널의 효율적 운영을 통한 경쟁력 확보가 중요시되고 있다. 컨테이너의 효율적인 적하는 선박의 효율과 양적하에 직접 관여하는 터미널 중심 장비인 켄트리 크레인의 효율을 극대화하도록 계획되어야 한다. 적하 문제는 선박의 크기와 각 터미널에서의 적하량에 종속되는 NP-hard 문제이다 본 연구에서는 컨테이너 적하 문제를 크게 두 개의 단계로 나누어 적하 계획을 수행한다. 무한한 경우의 수를 두 단계로 나눈 계획 시스템에 의해 크기를 줄인다. 첫 번째 단계는 컨테이너를 선박의 각 해치별로 배치하는 단계이고, 두 번째 단계는 각 해치별로 배정된 컨테이너를 특정 슬롯에 배치하는 것이다. 이렇게 분해된 문제의 각 단계에서 유전 알고리즘(Genetic Algorithm)을 사용하여 최적의 적하계획을 세운다. 그리고 정기 컨테이너선의 운항모형을 수립하고, 각 항구에서의 양·적하를 수행하여 구축된 시스템의 적합성을 시뮬레이션하여 평가한다

• Journal of Advanced Marine Engineering and Technology
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• 제31권5호
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• pp.599-605
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• 2007

This paper presents a method for identifying various systems based on input-output data and a real-coded genetic algorithm(RCGA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function of linearly separable parameters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The performance of the proposed algorithm is demonstrated through several simulations.

• 대한안전경영과학회지
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• 제13권1호
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• pp.133-142
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• 2011

Overhead facility design problem(OFDP) is one of the shortest rectilinear flow network problem(SRFNP)[4]. Genetic algorithm(GA), artificial immune system(AIS), population management genetic algorithm (PM) and greedy randomized adaptive search procedures (GRASP) were introduced to solve OFDP. A path matrix formed individual was designed to represent rectilinear path between each facility. An exchange crossover operator and an exchange mutation operator were introduced for OFDP. Computer programs for each algorithm were constructed to evaluate the performance of algorithms. Computation experiments were performed on the quality of solution and calculations time by using randomly generated test problems. The average object value of PM was the best of among four algorithms. The quality of solutions of AIS for the big sized problem were better than those of GA and GRASP. The solution quality of GRASP was the worst among four algorithms. Experimental results showed that the calculations time of GRASP was faster than any other algorithm. GA and PM had shown similar performance on calculation time and the calculation time of AIS was the worst.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.964-967
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• 1993

This paper presents an auto tuning method of fuzzy inference using Genetic Algorithm. The determination of membership functions by human experts is a difficult problem. Therefore, some auto-tuning methods have been proposed to reduce the time-consuming operations. However, the convergence of the tuning by the previous methods depends on the initial conditions of the fuzzy model. So, we proposes an auto tuning method for the fuzzy neural network by Genetic Algorithm (ATF system). This paper shows effectiveness of the ATF system by simulations.

• 정보기술과데이타베이스저널
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• 제7권1호
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• pp.42-53
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• 2000

The major goal of this research is to develop an optimal route search algorithm for an intelligent route guidance system, one sub-area of ITS. ITS stands for intelligent Transportation System. ITS offers a fundamental solution to various issues concerning transportation and it will eventually help comfortable and swift moves of drivers by receiving and transmitting information on humans, roads and automobiles. Genetic algorithm, and fuzzy logic are utilized in order to implement the proposed algorithm. Using genetic algorithm, the proposed algorithm searches shortest routes in terms of travel time in consideration of stochastic traffic volume, diverse turn constraints, etc. Then using fuzzy logic, it selects driver-preference optimal route among the candidate routes searched by GA, taking into account various driver's preferences such as difficulty degree of driving and surrounding scenery of road, etc. In order to evaluate this algorithm, a virtual road-traffic network DB with various road attributes is simulated, where the suggested algorithm promptly produces the best route for a driver with reference to his or her preferences.

• 대한산업공학회지
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• 제24권4호
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• pp.551-558
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• 1998

In this paper, a job shop scheduling system is developed which can cope with the changes of shop floor status with flexibility. This system suggests near optimal sequence of operations by using Genetic Algorithm which considers alternative process plans. The Genetic Algorithm proposed in this paper has some characteristics. The mutation rate is differentiated in order to enhance the chance to escape a local optimum and to assure the global optimum. And it employs the double gene structure to easily make the modeling of the shop floor. Finally, the quality of its solution and the computational time are examined in comparison with the method of a Simulated Annealing.

• 대한산업공학회지
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• 제27권2호
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• pp.140-149
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• 2001

The paper discusses the problem of finding the Origin-Destination(O-D) shortest paths in internodal transportation networks with time-schedule constraints. The shortest path problem on the internodal transportation network is concerned with finding a path with minimum distance, time, or cost from an origin to a destination using all possible transportation modalities. The time-schedule constraint requires that the departure time to travel from a transfer station to another node takes place only at one of pre-specified departure times. The scheduled departure times at the transfer station are the times when the passengers are allowed to leave the station to another node using the relative transportation modality. Therefore, the total time of a path in an internodal transportation network subject to time-schedule constraints includes traveling time and transfer waiting time. In this paper, a genetic algorithm (GA) approach is developed to deal with this problem. The effectiveness of the GA approach is evaluated using several test problems.