• Journal of KSNVE
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• v.10 no.5
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• pp.783-791
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• 2000

This describes a study on the support location optimizations of the beams using the genetic algorithm and the sensitivity analysis. The genetic algorithm is a probabilistic method searching the optimum at several points simultaneously and requiring only the values of the object and constraint functions. It has therefore more chances to find the global solution and can be applied to the various problems. Nevertheless, it has such a shortcoming that it takes too many calculations, because it is ineffective in local search. While the traditional method using sensitivity analysis is of great advantage in searching the near optimum. thus the combination of the two techniques will make use of the individual advantages, that is, the superiority in global searching form the genetic algorithm and that in local searching form the sensitivity analysis. In this thesis, for the practical applications, the analysis is conducted by FEB ; and as the shapes of structures are taken as the design variation, it requires re-meshing for every analysis. So if it is not properly controlled, the result of the analysis is affected and the optimized solution amy not be the real one. the method is efficiently applied to the problems which the traditional methods are not working properly.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.412-424
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• 2022

Since traditional derivative-based optimization for noisy simulation shows bad performance, evolutionary algorithms are considered as substitutes. Especially in case when outputs are binary, more simulation trials are needed to get near-optimal solution since the outputs are discrete and have high and heterogeneous variance. In this paper, we propose a genetic algorithm called SARAGA which adopts dynamic resampling and fitness approximation using surrogate. SARAGA reduces unnecessary numbers of expensive simulations to estimate success probabilities estimated from binary simulation outputs. SARAGA allocates number of samples to each solution dynamically and sometimes approximates the fitness without additional expensive experiments. Experimental results show that this novel approach is effective and proper hyper parameter choice of surrogate and resampling can improve the performance of algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1073-1082
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• 2023

Thermal sensors, also called thermal infrared wavelength sensors, measure temperature based on the intensity of infrared signals that reach the sensor. The infrared signals recognized by the sensor include infrared wavelength(0.7~3.0㎛) and radiant infrared wavelength(3.0~100㎛). Infrared(IR) wavelengths are divided into five bands: near infrared(NIR), shortwave infrared(SWIR), midwave infrared(MWIR), longwave infrared(LWIR), and far infrared(FIR). Most thermal sensors use the LWIR to capture images. Thermal sensors measure the temperature of the target in a non-contact manner, and the data can be affected by the sensor's viewing angle between the target and the sensor, the amount of atmospheric water vapor (humidity), air temperature, and ground conditions. In this study, the characteristics of three thermal imaging sensor models that are widely used for observation using unmanned aerial vehicles were evaluated, and the optimal application field was determined.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2973-2978
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• 2011

In this paper, a system is developed to support multi-attribute decision making for designing light-weight of rolling stock. Conceptual design of light-weight of rolling stock does not only mean reducing weight. It should be considered about some attributes like safety and environment, technology, etc. So technical attributes and needs of customers, manufacturers and management companies, passengers, should be reflected and qualitative evaluation methods are required. AHP(Analytical Hierarchy Process) and QFD(Quality Function Deployment) are used to decide weighted values of technical attributes and needs from customers. Finally, Alternatives for light-weight of rolling stock that are composed of alternatives of equipment are evaluated by TOPSIS(Technique for Order Preference by Similarity to Ideal Solution). A series of this process are made as a S/W. It could suggest a near-optimal alternative for light-weight of rolling stock.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.565-571
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• 2001

As a method of structural optimization, a practical algorithm based on the Taguchi method is developed. The Taguchi method is applied iteratively updating the level values of design variables. The design region is translated or reduced during optimization and by appropriate choice of reduction factor and initial level intervals, a near-optimum solution can be found very efficiently. To treat inequality constraints, a variable penalty method is utilized. A software system named 'DS/Taguchi' is developed by integrating the proposed algorithm and commercial finite element analysis codes on the parametric CAD platform. Two examples are taken to examine the performance of the proposed algorithm and the developed software system.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.13-13
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• 1988

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.551-556
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• 2002

High-performance concrete is defined as concrete that meets special combinations of performance and uniformity requirements that cannot always be achieved routinely using conventional constituents and normal mixing, placing, and curing practices. Ever since the term high-performance concrete was introduced into the industry, it had widely used in large-scale concrete construction that demands high-strength, high-flowability, and high-durability. To obtain such performances that cannot be obtained from conventional concrete and by the current method, a large number of trial mixes are required to select the desired combination of materials that meets special performance. In this paper, therefore, using genetic algorithm which is a global optimization technique modeled on biological evolutionary process-natural selection and natural genetics-and can be used to find a near optimal solution to a problem that may have many solutions, the new design method for high-performance concrete mixtures is suggested to reduce the number of trial mixtures with desired properties in the field test. Experimental and analytic investigations were carried out to develop the design method for high-performance concrete mixtures and to verify the proposed mix design.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.65-78
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• 2013

Circular shareholding is established when at least three member firms in a business group hold stock in other member firms and form a series of ownership in a circular way. Although there have been many studies which investigated a negative effect of circular shareholding on firm's value, few studies have discussed how to resolve the problem given complicated ownership structures of large business groups. This paper is based on a mixed integer programming model, which was proposed in the author's previous research and can identify the ownership share divested in order to resolve circular shareholding. Since the optimization model becomes too complicated for large business groups and requires a sophisticated software to solve it, we propose a simple heuristic method that can find a good approximate solution to the model. Its applications to twelve Korean large business groups show that the heuristic method is not just computationally attractive but also provides near-optimal solutions in most cases.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.2
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• pp.77-87
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• 1986

This paper presents a heuristic method for solving the discrete-time ordering problem with quantity discounts and deterministic, time-varying demand. This algorithm utilizes a variation of the incremental cost approach(ICA) to determine a near optimal solution. The ICA is the method which reduces the total cost with reduction of the number of orders by one. In order to reduce the number of orders, if the incremental cost for one of the periods is negative, the demand of the period should be purchased in its immediate preceding period. In order to test the performance of this algorithm, an experiment is conducted that involves a large number of test problems covering a wide variety of situations. The result of the experiment shows that the proposed algorithm has 80.5% better solutions than the adjusted part period algorithm(APPA), which is known to be the best heuristic method.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.285-293
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• 2004

In this study, a machine grouping problem for the formation of manufacturing cells is considered. We constructed the problem as minimizing manufacturing leadtime consisting of parts' processing, moving, and waiting time. Specifically, the main objective of the defined problem is established as minimizing inter-cell traffic in order to minimize the part's moving time. In addition, to reduce the waiting time of parts, the load balance among cells is implicitly included as constraints. Since this problem is well known as NP-complete and cannot be solved in polynomial time, a genetic algorithm is implemented to obtain solutions. Also, a local optimization algorithm is applied in order to improve the solution by the genetic algorithm. Several experiments show that the suggested algorithms guarantee near optimal solutions in a few seconds.