• 한국기계가공학회지
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• 제14권4호
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• pp.28-33
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• 2015

In this study, the optimal design for the support and the binding device for the protection of crops for the maximum allowable stress of the shape necessary to minimize volume has been proposed. Optimization of the support and the binding device for the crops should be designed to support businesses in terms of profit, in part to reduce the material, and to profit from the ease and speed of working that part of the farmers. We used CATIA for the mechanical design and the ANSYS program for the structural analysis. Additionally, the optimization was performed by PIAnO with seven design variables for the binding device and three parameters for the support. The weight method using a multi-objective function was also determined by the Pareto optimal solution. The volume of the binding device in the optimum design result was found to be reduced by 16%, from $2.278e-005m^3to1.912e-005m^3$. From the result, we confirmed the effectiveness of the design method proposed as a multi-objective function optimization problem.

• 대한산업공학회지
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• 제32권1호
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• pp.42-50
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• 2006

The multi-criteria group decision making (MCGDM) problem is to determine the best compromise solution in a set of competing alternatives that are evaluated under conflicting criteria by decision maker (DM)s. In this paper, we propose a mixed-integer programming (MIP) model to solve MCGDM. The existing method based on minimizing a distance measure such as Median Approach can not guarantee the best compromise solution because the element of median point vector is defined with respect to each criteria separately. However, by considering all criteria simultaneously, we generate median point that is better for locating the best compromise solution. We also utilize the concept of spatial dispersion index (SDI) to produce a threshold value, which is used as a guideline to choose either the Utopian Approach or the Median Approach. And we suggest using CBITP (Convex hull of individual maxima Based Interactive Tchebycheff Procedure) to provide DMs with various Pareto-optimal solutions so that DMs have broad range of selection.

• 한국해양공학회지
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• 제23권3호
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• pp.1-5
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• 2009

It is necessary to develop an efficient optimization technique to optimize engineering structures that have given design spaces, discrete design values, and several design goals. In this study, an optimum algorithm based on the genetic algorithm was applied to the multi-object problem to obtain an optimum solution that simultaneously minimizes the structural weight and construction cost of panel blocks in ship structures. The cost model was used in this study, which includes the cost of adjusting the weld-induced deformation and applying the deformation control methods, in addition to the cost of the material and the welding cost usually included in the normal cost model. By using the proposed cost model, more realistic optimum design results can be expected.

• International Journal of Reliability and Applications
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• 제12권2호
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• pp.79-94
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• 2011

Redundancy-reliability allocation problems in multi-stage series-parallel systems are addressed in this study. Fuzzy programming techniques are proposed for finding satisfactory solutions. First, a multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost. Due to the nature of uncertainty in the problem, the fuzzy set theory and technique are used to convert the deterministic multi-objective programming model into a fuzzy nonlinear programming problem. A heuristic method is developed to get satisfactory solutions for the fuzzy nonlinear programming problem. A Pareto optimal solution is found with maximal degree of satisfaction from the interception area of fuzzy sets. A case study that is related to the electronic control unit installed on aircraft engine over-speed protection system is used to implement the developed approach. Results suggest that the developed fuzzy multi-objective programming model can effectively resolve the fuzzy and uncertain problem when design goals and constraints are not clearly confirmed at the initial conceptual design phase.

• 한국습지학회지
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• 제26권1호
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• pp.10-20
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• 2024

습지의 보전 및 복원은 지속 가능한 인간 사회와 환경을 위한 필수적 과제로, 생물다양성 유지, 자연재해 저감, 기후 변화 완화 등 중요한 혜택을 제공한다. 본 연구는 습지 복원 및 조성을 위한 다양한 이해관계자들 간의 전략적 상호작용과 이익을 게임 이론을 통해 분석하고, 정책 결정에 중요한 근거를 제공하고자 한다. 이 연구에서는 대도시, 중소도시, 소도시의 세 가지 도시 유형에 대해 가상의 상황을 설정하고, 정부, 개발회사, 환경단체, 지역 주민 등의 이해관계자를 정의하였다. 각 이해관계자별 전략적 선택 사항을 도출하고, 습지생태 전문가들의 논의를 통해 보수행렬을 설정하였다. 이후 비협력적 게임 이론을 적용하여 내쉬 균형과 파레토 효율성을 분석하였다. 대도시에서는 '습지 보존'과 '친환경 개발', 중소도시에서는 다양한 전략들, 그리고 소도시에서는 '친환경 개발'이 이해당사자 모두에게 이득이 되는 해결책으로 나타났다. 파레토 효율성 분석 결과, 각 도시 유형별로 습지 관리와 관련하여 이해당사자들 간의 최적의 해결책이 어떻게 달라질 수 있는지를 보여주었다. 도시 유형별로 습지 보존, 친환경 개발, 습지 복원 사업이 각각 중요하게 부각되었다. 이에 따라 정책 입안자들은 환경 보호와 도시 개발의 조화를 이루는 규제와 인센티브를 마련하고, 지역 사회의 참여를 촉진하는 프로그램을 고려해야 한다. 이해당사자별 역할과 전략을 통해 습지 보존과 지역 경제 발전을 동시에 촉진하는 방안을 모색해야 한다. 전략별 장단점을 이해하고, 이를 바탕으로 보다 효과적인 정책 결정을 내리는 것이 중요하다.

• 한국수자원학회논문집
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• 제41권12호
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• pp.1211-1218
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• 2008

본 연구의 목적은 다목적 유전자 알고리즘을 이용하여 우수유출 저류지를 소유역에 분담하여 설치 계획하는데있다. 이를 위해 우수유출 저류지의 위치 및 규모를 최적화하기 위한 모형을 개발하였다. 이 모형은 크게 2가지로 나뉘어 지는데, 유역유출모형과 최적해를 구하기 위하여 도입한 다목적 유전자 알고리즘(MOGAs)이다. 이러한 최적화 모형을 모의하기 위하여 목적함수는 첨두유출량과 저류지 저류용량의 함수로 설정하고, 제한조건은 기본적으로 구조적 제한과 저류용량 및 운영목적에 따라 설정하였다. 본 연구를 위해 만들어진 최적화 모형은 안양시 관양유역에 실제 적용해 보았으며, 그 결과 주어진 제약조건내에서 상류지역에 저류지가 설치되는 경우가 최적값으로 나타난 것을 통해 적절하게 잘 모의된 것으로 보인다. 그밖에 On-line 및 Off-line 저류지가 동시에 건설되는 경우에도 함께 최적화 모의가 가능한지도 살펴보았다. 본 연구를 통해 제시한 방법론은 향후 도시유역내의 홍수피해 저감계획시 활용될 수 있을 것이다.

• Journal of Computational Design and Engineering
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• 제1권4호
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• pp.256-265
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• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

• Earthquakes and Structures
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• 제7권3호
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• pp.271-294
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• 2014

In recent years, along with the advances made in performance-based design optimization, the need for fast calculation of response parameters in dynamic analysis procedures has become an important issue. The main problem in this field is the extremely high computational demand of time-history analyses which may convert the solution algorithm to illogical ones. Two simplifying strategies have shown to be very effective in tackling this problem; first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication, second, wavelet analysis of earthquake records decreasing the number of acceleration points involved in time-history loading. In this paper, we try to develop an efficient framework, using both strategies, to solve the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency (FEMA) recommended design specifications. The results from numerical application of the proposed framework demonstrate the capabilities of the framework in solving the present multi-objective optimization problem.

• 지식경영연구
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• 제20권3호
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• pp.39-47
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• 2019

Response surface methodology (RSM) is a group of statistical modeling and optimization methods to improve the quality of design systematically in the quality engineering field. Its final goal is to identify the optimal setting of input variables optimizing a response. RSM is a kind of knowledge management tool since it studies a manufacturing or service process and extracts an important knowledge about it. In a real problem of RSM, it is a quite frequent situation that considers multiple responses simultaneously. To date, many approaches are proposed for solving (i.e., optimizing) a multi-response problem: process capability function approach, desirability function approach, loss function approach, and so on. The process capability function approach first estimates the mean and standard deviation models of each response. Then, it derives an individual process capability function for each response. The overall process capability function is obtained by aggregating the individual process capability function. The optimal setting is given by maximizing the overall process capability function. The existing process capability function methods usually use the arithmetic mean or geometric mean as an aggregation operator. However, these operators do not guarantee the Pareto optimality of their solution. Moreover, they may bring out an unacceptable result in terms of individual process capability function values. In this paper, we propose a maximin-based process capability function method which uses a maximin criterion as an aggregation operator. The proposed method is illustrated through a well-known multiresponse problem.

• Advances in aircraft and spacecraft science
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• 제10권3호
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• pp.245-256
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• 2023

In order to improve aerodynamic performance of multi-stage axial flow turbines used in aircraft engines, a one-dimensional aerodynamic design and optimization framework is constructed. In the method, flow path is generated by solving mass continuation and energy conservation with loss computed by the Craig & Cox model; Also real gas properties has been taken into consideration. To obtain an optimal result, a multi-objective genetic algorithm is used to optimize the efficiencies and determine values of various design variables; Final design can be selected from obtained Pareto optimal solution sets. A three-stage axial turbine is used to verify the effectiveness of the developed optimization framework, and designs are checked by three-dimensional CFD simulation. Results show that the aerodynamic performance of the optimized turbine has been significantly improved at design point, with the total-to-total efficiency increased by 1.17% and the total-to-static efficiency increased by 1.48%. As for the off-design performance, the optimized one is improved at all working points except those at small mass flow.