• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1997년도 춘계 학술대회 발표집
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• pp.3-7
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• 1997

Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. "Simulation Optimization" technique is used to optimize the expressed in analytical of mathematical models. In this research, we will study Reverse-Simulation optimization method which is quite different from current simulation optimization methods in literature. We will focus on the on-line determination of steady-state method which is very important issue in Reverse-Simulation optimization, and the construction of Reverse-Simulation algorithm with expert systems. Especially, in the case of multiple objectives because of the dependency of simulation model, all objectives do not satisfied simulataneously. In this paper, therefore, we process simulation optimization using objectives with priority to optimize multiple objectives under single run.ingle run.

• 한국시뮬레이션학회논문지
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• 제6권1호
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• pp.71-84
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• 1997

For many optimization problems where some of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. Many, if not most, simulation optimization problems have multiple aspects. Historically, multiple aspects have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple aspects. In this paper we propose a MAGA (Multiple Aspects-based Genetic Algorithm) as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.110.4-110
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• 2002

In practical control area, there are many examples with multiple objectives which may conflict or compete with each other like overhead crane control, automatic train operation, and refuse incinerator plant control, etc. These kinds of control problems are called multiobjective control problems, where it is difficult to provide the desired performance with control strategies based on single-objective optimization. Because the conventional control theories usually treat the control problem as the single objective optimization problem , the methods are not adequate to treat the multiobjective control problems. Particularly, in case of large scale systems or ill-defined systems, the multiple obj..

• Earthquakes and Structures
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• 제23권1호
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• pp.35-57
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• 2022

This article presents a computationally efficient framework for multi-objective seismic design optimization of steel moment-resisting frame (MRF) structures based on the nonlinear dynamic analysis procedure. This framework employs the uniform damage distribution philosophy to minimize the weight (initial cost) of the structure at different levels of damage. The preliminary framework was recently proposed by the authors based on the single excitation and the nonlinear static (pushover) analysis procedure, in which the effects of record-to-record variability as well as higher-order vibration modes were neglected. The present study investigates the reliability of the previous framework by extending the proposed algorithm using the nonlinear dynamic design procedure (optimization under multiple ground motions). Three benchmark structures, including 4-, 8-, and 12-story steel MRFs, representing the behavior of low-, mid-, and high-rise buildings, are utilized to evaluate the proposed framework. The total weight of the structure and the maximum inter-story drift ratio (IDRmax) resulting from the average response of the structure to a set of seven ground motion records are considered as two conflicting objectives for the optimization problem and are simultaneously minimized. The results of this study indicate that the optimization under several ground motions leads to almost similar outcomes in terms of optimization objectives to those are obtained from optimization under pushover analysis. However, investigation of optimal designs under a suite of 22 earthquake records reveals that the damage distribution in buildings designed by the nonlinear dynamic-based procedure is closer to the uniform distribution (desired target during the optimization process) compared to those designed according to the pushover procedure.

• 대한전자공학회논문지SP
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• 제44권2호
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• pp.108-115
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• 2007

본 논문에서는 최적화 알고리즘의 속도를 향상시킬 수 있는 방안으로 설계자가 원하는 목적함수들의 수렴 범위를 Goal로 설정하여 최적화를 수행하는 GBNSGA(Goal-Pareto based Non-dominated Sorting Genetic Algorithm)를 제안한다. 많은 공학문제들은 하나의 목표치를 충족하는 해를 찾는 것이 아니라 다수 목적함수들을 충족하는 해를 찾는 것이 일반적이다 특히, 이러한 목적함수들은 서로 상충적인 관계를 갖는 경우가 대부분이기 때문에 모든 목적함수들을 만족하는 유일해를 찾는 것은 거의 불가능하다. 그 대안으로 일부 목적을 희생하며 설계에 부합되는 최적해를 찾는 파레토(Pareto) 방식의 최적화 알고리즘들에 대한 많은 연구가 진행되었다. 본 논문에서는 이러한 파레토 기반의 최적화 알고리즘들의 성능 향상을 도모하기 위하여 설계자의 목적을 파레토 할당에 반영하는 GBNSGA를 제안하고, 그 성능을 NSGA와 weighted-sum 접근 방식과의 비교를 통해 그 우수성을 검증하였다.

• Journal of Mechanical Science and Technology
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• 제19권5호
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• pp.1080-1086
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• 2005

The preliminary design optimization of multi-stage spur gear reduction units has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive and aerospace) require high-performance gear reduction units. There are multiple objectives in the optimal design of multi-stage spur gear reduction unit, such as minimizing the volume and maximizing the surface fatigue life. It is reasonable to formulate the design of spur gear reduction unit as a multi-objective optimization problem, and find an appropriate approach to solve it. In this paper an interactive physical programming approach is developed to place physical programming into an interactive framework in a natural way. Class functions, which are used to represent the designer's preferences on design objectives, are fixed during the interactive physical programming procedure. After a Pareto solution is generated, a preference offset is added into the class function of each objective based on whether the designer would like to improve this objective or sacrifice the objective so as to improve other objectives. The preference offsets are adjusted during the interactive physical programming procedure, and an optimal solution that satisfies the designer's preferences is supposed to be obtained by the end of the procedure. An optimization problem of three-stage spur gear reduction unit is given to illustrate the effectiveness of the proposed approach.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제3권1호
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• pp.73-81
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• 1996

Multiple objective programming has been a popular research area since 1970. The pervasiveness of multiple objective in decision problems have led to explosive growth during the 1980's. Several approaches (interactive methods, feasible direction methods, criterion weight space methods, Lagrange multiplies methods, etc) have been developed for solving decision problems having multiple objectives. However there are still many mathematically challengings including multiple objective integer, nonlinear optimization problems which require further mathematically oriented research. (omitted)

• 한국유체기계학회 논문집
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• 제11권1호
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• pp.52-60
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• 2008

Staggered dimples printed on opposite walls of an internal cooling channel are formulated numerically and optimized to enhance heat transfer performance. Nusselt number and friction factor based objectives are considered and a weighted average surrogate model is used to approximate the data generated by numerical simulation. The dimpled channel shape is defined by three geometric design variables, and the design point within design space are selected using Latin hypercube sampling. A weighted-sum method for multi-objective optimization is applied to integrate multiple objectives into a single objective. By the optimization, the objective function value is improved largely and heat transfer rate is increase much higher than pressure loss increase due to shape deformation. Channel with vertically non-symmetric optimum dimples is tested and found that the best appears if dimples on opposite wall are displaced by one quarter of dimple spacing.

• 국제학술발표논문집
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• The 2th International Conference on Construction Engineering and Project Management
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• pp.642-652
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• 2007

Many contracting firms and project managers in the construction industry have started to utilize multi objective optimization methods to handle multiple conflicting goals for completing the project within the stipulated time and budget with required quality and safety. These optimization methods have increased the pressure on decision makers to search for an optimal resources utilization plan that optimizes simultaneously the total project cost, completion time, and crashing cost by considering indirect cost, contractual penalty cost etc., practically charging them in terms of direct cost of the project which is fuzzy in nature. This paper presents a multiple fuzzy goal programming model (MFGP) that supports decision makers in performing the challenging task. The model incorporates the fuzziness which stems from the imprecise aspiration levels attained by the decision maker to these objectives that are quantified through fuzzy linear membership function. The membership values of these objectives are then maximized which forms the fuzzy decision. The problem is solved using LINGO 8 optimization solver and the best compromise solution is identified. Comparison between solutions of MFGP, fuzzy multi objective linear programming (FMOLP) and multiple goal programming (MGP) are also presented. Additionally, an interactive decision making process is developed to enable the decision maker to interact with the system in modifying the fuzzy data and model parameters until a satisfactory solution is obtained. A case study is considered to demonstrate the feasibility of the proposed model for optimization of project network parameters in the construction industry.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.660-667
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• 2003

In this study, a Multiple Objective Mixed Integer Programming (MOMIP) Model is developed for sewer rehabilitation planning by considering cost, inflow/infiltration. A sewer rehabilitation planning model is required to decide the economic life of the sewer by considering trade-off between cost and inflow/infiltration. And it is required to find the optimal rehabilitation timing, according to the cost effectiveness of each sewer rehabilitation within the budget. To develop such a model, a multiple objective mixed integer programming model is formulated based on network flow optimization. The network is composed of state nodes and arcs. The state nodes represent the remaining life and the arcs represent the change of the state. The model consider multiple objectives which are cost minimization and minimization of inflow/infiltration. Using the multiple objective optimization, the trade-off between the cost and inflow/infiltration is presented to the planner so that a proper sewer rehabilitation plan can be selected.