• 한국국방경영분석학회지
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• 제13권1호
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• pp.116-124
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• 1987

This paper studies to develop the procedure which is combined by the progressive goals and progressive weights generation method. This procedure minimizes the number of questions the decision maker has to make, and also satisfies the generated minimum goal of each objective function. With the procedure developed, we are able to improve the previous multiple objective linear programming techniques in two points.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
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• pp.443-448
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• 2000

In this paper, multiphase optimization of machine structure is presented. The goal of first step is to obtain (i) light weight, (ii) rigidity statically. In this step, multiple optimization problem with two objective functions is treated using Pareto Genetic Algorithm. Where two objective functions are weight of the structure, and static compliance. The method is applied to a new machine structure design.

• 한국국방경영분석학회지
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• 제24권1호
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• pp.157-175
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• 1998

This paper develops multiple objective manpower planning model in order to design and adjust manpower structure and flow when advance rate for officer's native is considered. The state transition in manpower structure is analyzed using Markov chains. Multiple objectives in the model are security of advance rate, satisfaction of rank's number of personnel, and stability of the number of recruit personnel for officer's native. Trade - off of these objectives is made to evaluate manpower structure and flow. Solutions of this model are obtained by LINGO package.

• 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.

• Smart Structures and Systems
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• 제26권5호
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• pp.545-558
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• 2020

A design method of a Multiple Tuned Mass Damper (MTMD) system is presented for wind induced vibration control of a cable-supported roof structure. Modal contribution analysis is carried out to determine the dominating modes of the structure for the MTMD design. Two MTMD systems are developed for two most dominating modes. Each MTMD system is composed of multiple TMDs with small masses spread at multiple locations with large responses in the corresponding mode. Frequencies of TMDs are distributed uniformly within a range around the dominating frequencies of the roof structure to enhance the robustness of the MTMD system against uncertainties of structural frequencies. Parameter optimizations are carried out by minimizing objective functions regarding the structural responses, TMD strokes, robustness and mass cost. Two optimization approaches are used: Single Objective Approach (SOA) using Sequential Quadratic Programming (SQP) with multi-start method and Multi-Objective Approach (MOA) using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The computation efficiency of the MOA is found to be superior to the SOA with consistent optimization results. A Pareto optimal front is obtained regarding the control performance and the total weight of the TMDs, from which several specific design options are proposed. The final design may be selected based on the Pareto optimal front and other engineering factors.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.170-175
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• 2001

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multiple objective genetic algorithm (MOGA), and a sample test case is presented to show the effects of optimizing the sequence with MOGA.

• 산업경영시스템학회지
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• 제21권46호
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• pp.207-219
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• 1998

Multiple Objective Programming(MOP) has been suggested for the solution of completed decision problems. Decision analysis in numerous areas, including energy and environmental planning, necessarily requires consideration of multiple conflicting objectives, MOP has been successfully applied to a number of these problems. The objective of this paper is to present a MOP process which are integrated model with the Input-Output(I-O) analysis for energy and environment planning in industrial sectors. In the model, three objectives are observed such as (1) value added (2) total energy consumption and (3) environmental impacts. Special emphasis is placed on the police implications of industrial structures.

• 한국수자원학회논문집
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• 제36권4호
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• pp.547-559
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• 2003

본 연구에서는 체계적이고 효율적인 하수관거 정비 계획 수립을 위하여 하수관거의 경제적 가치와 불명수 발생량 및 예산 제약 등을 고려한 최적화 모형을 개발 하였다. 하수관거의 최적 정비 계획 모형은 비용 및 환경오염을 일으키는 불명수 발생 사이의 상관관계를 적절히 판단하여 최적의 경제적 사용연수를 결정하여야 하며, 예산 제약 및 각 하수관거와 배수구역의 비용 대비 효과 등을 고려하여 최적의 정비 시점 및 방법을 결정하여야 한다. 이를 위하여 본 연구에서는 관거의 잔존 수명을 상태 노드로 표현하고, 정비에 따른 잔존 수명의 변화를 아크로 표현한 최소비용 네트워크 흐름 최적화(Network Flow Optimization)모형을 구성하였으며, 이를 기초로 예산 제약 및 하수관거 시스템의 정비 특성을 고려한 제약식을 추가한 다중 목적 혼합 정수계획법(Multiple Objective Mixed Integer Programming, MOMIP)을 수립하였다. 이때 모형의 목적식은 비용 최소화 목적과 함께 불명수 발생량 최소화 목적을 추가하여, 의사결정자에게 비용과 불명수 발생량 사이의 영향 관계를 보여줌으로써 적절한 하수관거 정비 계획을 선택할 수 있도록 하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.997-1000
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• 2005

Dynamic job shop environment requires not only more flexible capabilities of a CAPP system but higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations to be performed by predicting the machine loads. The developed algorithm is based on the multiple objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as Pareto-optimal solutions). The objective shows a combination of the minimization of part movement and the maximization of machine utility balance. The algorithm is characterized by a new and efficient method for nondominated sorting, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II.

• 산업공학
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• 제25권2호
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• pp.264-275
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• 2012

The process of semiconductor(IC Package) manufacturing usually includes lots of complex and sequential processes. Many kinds of equipments are installed with the mixed concept of serial and parallel manufacturing system. The business environments of the semiconductor industry have been changed frequently, because new technologies are developed continuously. It is the main reason of new investment plan and layout consideration. However, it is difficult to change the layout after installation, because the major equipments are expensive and difficult to move. Furthermore, it is usually a multiple-objective problem. Thus, new investment or layout change should be carefully considered when the production environments likewise product mix and production quantity are changed. This paper introduces a simulation case study of a Korean company that produces packaging substrates(especially lead frames) and requires multi-objective decision support. $QUEST^{(R)}$ is used for simulation modelling and AHP(Analytic Hierarchy Process) and DEA(Data Envelopment Analysis) are used for weighting of qualitative performance measures and solving multiple-objective layout problem, respectively.