• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.115-127
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• 2020

In this paper, a sustainable close-loop supply chain (SCLSC) model is proposed for effectively managing the production, distribution and handling process of mobile phone. The proposed SCLSC model aims at maximizing total profit as economic factor, minimizing total CO₂ emission amount as environmental factor, and maximizing social influence as social factor in order to reinforce sustainability in it. Since these three factors are represented as each objective function in modeling, the proposed SCLSC model can be taken into consideration as a multi-objective optimization problem and solved using a hybrid genetic algorithm (HGA) approach. In numerical experiment, three different scales of the SCLSC model are presented and the efficiency of the HGA approach is proved using various measures of performance.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.1-7
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• 2009

This paper presents a process routing (PR) algorithm with multiple objectives. PR determines the optimum sequence of operations for transforming a raw material into a completed part within the available machining resources. In any computer aided process planning (CAPP) system, selection of the machining operation sequence is one of the most critical activities for manufacturing a part and for the technical specification in the part drawing. Here, the goal could be to generate the sequence that optimizes production time, production cost, machine utilization or with multiple these criteria. The Pareto Stratum Niche Cubicle (PS NC) GA has been adopted to find the optimum sequence of operations that optimize two conflicting criteria; production cost and production quality. The numerical analysis shows that the proposed PS NC GA is both effective and efficient to the PR problem.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.10
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• pp.938-946
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• 2005

Shape optimization of internally finned circular tube has been peformed for periodically fully developed turbulent flow and heat transfer. The physical domain considered in this study is very complicated due to periodic boundary conditions both streamwise and circumferential directions. Therefore, Pareto frontier sets of a heat exchanger can be acquired by coupling the CFD and the multi-objective genetic algorithm, which is a global optimization technique. The optimal values of fin widths $(d_1,\;d_2)$ and fin height (H) are numerically obtained by minimizing the pressure loss and maximizing the heat transfer rate within ranges of $d_1=0.2\sim1.5\;mm,\;d_2=0.2\sun1.5\;mm,\;and\;H=0.2\sim1.5\;mm$. The optimal values of the design variables are acquired after the fifth generation and also compared to those of a local optimization algorithm for the same geometry and conditions.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2639-2641
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• 2003

유전자 알고리즘은 다윈의 자연선택설과 유전자의 진화 개념을 이용한 적응 탐색 알고리즘으로 적용하고자 하는 문제의 매개 변수를 유전자와 비슷한 데이터 구조로 부호화하고, 유전 연산자를 이용하여 문제의 해답을 찾는 알고리즘이다. 최근 유전자 알고리즘은 이러한 복수개의 목적 함수를 최적화 하기 위한 다중 최적화 문제를 위한 최적화 기술로서의 관심이 크게 다루어지고 있으며 전송 문제, 생산 공정 문제 계획 등과 같은 다목적 함수를 다루는 많은 응용 부분에 대해 적용되고 있다. 본 논문에서는 기본적인 다중 목적 함수용 예와 Gen과 Kim이 제안한 네트워크 신뢰도를 고려한 연결 비용과 메시지 지연을 고려한 이중 구속 통신망 설계 문제를 가지고 가중치 합과 여러 가지 파레토 방법들을 비교하고 연구 검토 하고자 한다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.419-426
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• 2020

The structural design of the steel eccentrically braced frame (EBF) was developed and analyzed in this study through multiobjective optimization (MOO). For the optimal design, NSGA-II which is one of the genetic algorithms was utilized. The amount of structure and interfloor displacement were selected as the objective functions of the MOO. The constraints include strength ratio and rotation angle of the link, which are required by structural standards and have forms of the penalty function such that the values of the objective functions increase drastically when a condition is violated. The regulations in the code provision for the EBF system are based on the concept of capacity design, that is, only the link members are allowed to yield, whereas the remaining members are intended to withstand the member forces within their elastic ranges. However, although the pareto front obtained from MOO satisfies the regulations in the code provision, the actual nonlinear behavior shows that the plastic deformation is concentrated in the link member of a certain story, resulting in the formation of a soft story, which violates the capacity design concept in the design code. To address this problem, another constraint based on the Eurocode was added to ensure that the maximum values of the shear overstrength factors of all links did not exceed 1.25 times the minimum values. When this constraint was added, it was observed that the resulting pareto front complied with both the design regulations and capacity design concept. Ratios of the link length to beam span ranged from 10% to 14%, which was within the category of shear links. The overall design is dominated by the constraint on the link's overstrength factor ratio. Design characteristics required by the design code, such as interstory drift and member strength ratios, were conservatively compared to the allowable values.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.31-41
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• 2003

This paper deals with a cell formation problem for a set of m-machines and n-processing parts. Generally, a cell formation problem is known as NP-completeness. Hence the cell formation problem with multiple objectives is more difficult than single objective problem. The paper considers multiple objectives; minimize number of intercell movements, minimize intracell workload variation and minimize intercell workload variation. We propose a multiple objective genetic algorithms(MOGA) resolving the mentioned three objectives. The MOGA procedure adopted Pareto optimal solution for selection method for next generation and the concept of Euclidean distance from the ideal and negative ideal solution for fitness test of a individual. As we consider several weights, decision maker will be reflected his consideration by adjusting high weights for important objective. A numerical example is given for a comparative analysis with the results of other research.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.125-134
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• 2022

Recently, there are an increased the number of reports on the misuse or malicious use of an UAS. Thus, many researchers are studying on defense schemes for UAS by developing or improving C-UAS sensor technology. However, the wrong placement of sensors may lead to a defense failure since the proper placement of sensors is critical for UAS defense. In this study, a multi-object optimization model for C-UAS sensor placement in an air base is proposed. To address the issue, we define two objective functions: the intersection ratio of interested area and the minimum detection range and try to find the optimized placement of sensors that maximizes the two functions. C-UAS placement model is designed using a NSGA-II algorithm, and through experiments and analyses the possibility of its optimization is verified.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.5
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• pp.435-441
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• 2015

In this study, a method for optimal design of impeller and diffuser blades in the centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was evaluated. A numerical simulation was conducted using ANSYS CFX with various values of impeller and diffuser parameters, which consist of leading edge (LE) angle, trailing edge (TE) angle, and blade thickness. Each of the parameters was divided into three levels. A total of 45 design points were planned using central composite design (CCD), which is one of the design of experiment (DOE) techniques. Response surfaces that were generated on the basis of the results of DOE were used to determine the optimal shape of impeller and diffuser blade. The entire process of optimization was conducted using ANSYS Design Xplorer (DX). Through the optimization, isentropic efficiency and pressure recovery coefficient, which are the main performance parameters of the centrifugal compressor, were increased by 0.3 and 5, respectively.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.163-171
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• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.