• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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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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• v.3 no.1
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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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• v.8 no.3
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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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• v.20 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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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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• v.9 no.2
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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).

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.65-75
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• 2003

In these days, public transportation has become important because of serious traffic congestion. But. there are few researches in public transportation compared with researches in auto. Accordingly, the purpose of paper is development of transit assignment model, which considers features of public transportation, time table, transfer capacity of vehicle, common line, etc. The transit assignment model developed in this paper is composed of two parts. One part is search for optimum path, the other part is network loading. A Genetic algorithm has been developed in order to search for alternative shortest path set. After the shortest paths have been obtained in the genetic algorithm, Logit-base stochastic loading model has been used to obtain the assigned volumes.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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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.

• Korean Journal of Biological Psychiatry
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• v.15 no.2
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• pp.83-91
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• 2008

Objectives : Neurological soft signs have been regarded as endophenotypes associated with the genetic basis of schizophrenia. This study was to investigate the intra-familial correlations of the neurological soft signs according to their genetic loading. Methods : Schizophrenic patients(N=14) were included, who had one parent with a family history of schizophrenia and the other without it. Genetic loading was determined by the patient's family history of schizophrenia using the Family Interview for Genetic Studies(FIGS). These parents were subdivided into two groups. The first group was designated as 'presumed carriers'(N=9) of genetic loading, who had one or more schizophreic firstor second-degree relatives. The second group was designated as 'presumed non-carriers'(N=11) of genetic loading, who had no schizophrenic first- or second-degree relatives. Normal controls(N=12) consisted of people without schizophrenic relatives. NSS were evaluated using the Neurological Evaluation Scale-Korean Version (NES-K), and the intra-familial correlations of NSS were tested using the Intra-Class Coefficients(ICC) method. Results : The scores of Motor Coordination subdimension of NES-K were significantly correlated between the patients and their presumed carriers(ICC=.804, p=.016), but not significantly correlated between the patients and their presumed noncarriers. In other subdimensions of NES-K, no significant correlation were found between the patients and their parents regardless of the genetic loading. But, there were no statistically significant differences in the scores of Motor Coordination subdimension of NES-K between the patients and controls. Conclusion : This study did not prove that the neurological soft signs might be an endophenotype of schizophrenia that cosegregate with the genetic loading. The future study using more subjects than this would be needed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.186-191
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• 2020

In this paper, we proposed an optimization technique for efficiently placing vehicles on decks in a vehicle-carrying ship to efficiently handle loading and unloading. For this purpose, we utilized the transformation method of the XML data representing the ship's spatial information, merging and branching algorithm and genetic algorithm, and implemented the function to visualize the optimized vehicle placement results. The techniques of selection, crossover, mutation, and elite preservation, which are used in the conventional genetic algorithms, are used. In particular, the vehicle placement optimization method is proposed by merging and branching the ship space for the vehicle loading. The experimental results show that the proposed merging and branching method is effective for the optimization process that is difficult to optimize with the existing genetic algorithm alone. In addition, visualization results show vehicle layout results in the form of drawings so that experts can easily determine the efficiency of the layout results.