• 제어로봇시스템학회논문지
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• 제17권9호
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• pp.932-940
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• 2011

This paper presents an integrated analysis of supply chain and financing decisions of multi-national corporation. We construct a model in which multiple currency storage units are installed to manage the currency flows associated with multi-national supply chain activities such as raw material procurement, process operation, inventory control, transportation and finished product sales. Core contribution of this study is to quantitatively investigate the influence of macroscopic economic factors such as exchange rates and taxes on operational decisions. The supply chain is modeled by the Process-Storage Network with recycle streams. The objective function of the optimization is minimizing the opportunity costs of annualized capital investments and currency/material inventories minus the benefit to stockholders interpreted by home currency. The major constraints of the optimization are that the material and currency storage units must not be depleted. A production and inventory analysis formulation, the periodic square wave (PSW) model, provides useful expressions for the upper/lower bounds and average levels of the currency and material inventory holdups. The expressions for the Kuhn-Tucker conditions of the optimization problem are reduced to a subproblem and analytical lot sizing equations. The procurement, production, transportation and financial transaction lot sizes can be determined by analytical expressions after the average flow rates are already known. We show that, when corporate income tax is taken into consideration, the optimal production lot and storage sizes are smaller than is the case when such factors are not considered typically by 20 %.

• 한국추진공학회지
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• 제4권2호
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• pp.21-30
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• 2000

본 연구에서는 램 가속기의 성능 향상을 위한 수치최적화를 수행하였다. 일정한 형상과 질량을 가진 탄체를 최초의 속도 $V_o$로부터 목표 속도 $V_e$로 가속시킬 때까지의 최소의 램 가속관 길이를 탐색하는 것을 목표로 하였고 $H_2$, $O_2$, $N_2$로 구성된 예 혼합기의 각 화학종의 몰수를 설계변수로서 채택하였다. 목적함수와 구속조건은 설계 과정에서 선형화 하여 구배법과 SLP기법을 적용하였다. 내부유동은 이차원 비점성 유동을 가정하고 화학반응의 해석은 8단계 7화학종 모델을 적용하였다. 가속관 길이의 결정을 위하여 램 가속관 내부의 유동은 준 정상상태로 가정하고 몇 개의 동일 구간으로 분할하여 각 속도에서의 추력 계수와 가속도를 동시에 구하여 전 속도 영역에 대하여 수치 적분하였다. 본 연구를 통하여 7회의 설계 반복으로 가속관의 길이를 19% 감소시켰다. 본 연구의 결과로부터 다단계, 다 화학종의 램 가속기의 설계최적화 문제에 직접적으로 적용할 수 있음을 확인하였다.

• 대한기계학회논문집A
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• 제34권8호
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• pp.1129-1136
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• 2010

체결클러치에 부착된 댐퍼스프링은 유체커플링에서 직결로 변환될 때 발생하는 엔진 토크의 진동을 흡수하는 역할을 한다. 본 연구에서는 체결클러치의 성능을 좌우하는 압축스프링 및 지지 판 구조물의 최적설계를 통해서 새로운 설계형상을 제안하였다. 체결클러치와 연결된 엔진, 변속기, 구동축 및 휠, 차체질량 등 주요 부품들을 다 포함하는 다물체 동역학모델을 구성하여 공진 회피에 필요한 스프링상수를 계산하였다. 또한 어닐링 모사법에 의한 스프링 최적설계코드를 개발한 후 스프링상수, 최대충격토크, 수축각도, 스프링개수, 피로강도 등을 입력하여 압축 스프링의 사양을 최적화하였다. 이들 스프링을 지지하는 3 가지의 판에 대해서 컴플라이언스를 최소화하고 체적비를 0.3 이하로 하는 위상최적화를 수행하여 새로운 형상을 제안하였다.

• 전자공학회논문지
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• 제52권8호
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• pp.3-9
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• 2015

본 논문에서는 사용 가능한 최대 송신 전력과 만족해야 하는 최소 전송률에 대한 제한 조건 아래에서, maximal ratio transmission (MRT) 기법 사용 시 다중 사용자 다중 안테나 하향링크 시스템의 에너지 효율을 최대화하는 최적의 안테나 수와 송신 전력을 찾는 알고리즘을 제시한다. 순시 채널에 대한 최적화 문제는 직접 풀기 어려우므로 단말 간 채널의 독립성, 평균 채널 이득, 평균 path loss를 이용하여 근사한다. 근사된 에너지 효율에 대한 최적화 문제는 두 개의 변수를 동시에 고려해야 하는 2차원 최적화 문제가 된다. 우리는 이러한 2차원 최적화 문제를 라그랑지 승수법과 제안하는 알고리즘을 통하여 최적의 안테나 수와 송신 전력을 구한다. 실험을 통해, 제안하는 알고리즘으로 구한 최적의 송신 안테나 수와 송신 전력이 exhaustive search로 찾은 값과 근사함을 확인한다.

• International Journal of Control, Automation, and Systems
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• 제2권4호
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• 한국유체기계학회 논문집
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• 제16권1호
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• pp.11-16
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• 2013

A numerical study was conducted to improve the performance of an impeller of centrifugal compressor. Nine design variables were chosen with constraints. Only meridional contours and blade profile were adjusted. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. At first, ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After $7^{th}$ generation, the prediction difference of efficiency and pressure ratio between ANN and CFD was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1837-1860
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• 2020

We propose a general framework for studying resource allocation problem and the tradeoff between spectral efficiency (SE) and energy efficiency (EE) for downlink traffic in power domain-non-orthogonal multiple access (PD-NOMA) and device to device (D2D) based heterogeneous cloud radio access networks (H-CRANs) under imperfect channel state information (CSI). The aim is jointly optimize radio remote head (RRH) selection, spectrum allocation and power control, which is formulated as a multi-objective optimization (MOO) problem that can be solved with weighted Tchebycheff method. We propose a low-complexity algorithm to solve user association, spectrum allocation and power coordination separately. We first compute the CSI for RRHs. Then we study allocating the cell users (CUs) and D2D groups to different subchannels by constructing a bipartite graph and Hungrarian algorithm. To solve the power control and EE-SE tradeoff problems, we decompose the target function into two subproblems. Then, we utilize successive convex program approach to lower the computational complexity. Moreover, we use Lagrangian method and KKT conditions to find the global optimum with low complexity, and get a fast convergence by subgradient method. Numerical simulation results demonstrate that by using PD-NOMA technique and H-CRAN with D2D communications, the system gets good EE-SE tradeoff performance.

• 경영과학
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• 제28권1호
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• pp.141-158
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• 2011

This study concerns with an applicability of the management science approach to the score adjustment among the College Scholastic Aptitude Test(CSAT) optional subjects. A linear programming model is developed to minimize the sum of score distortions between optional subjects. Based on the analysis of the 377,089 CSAT(2010) applicants' performances in social science test section, this study proposes a new approach for the score equating or linking method of the educational measurement theory. This study makes up for the weak points in the previous linear programming model. First, the model utilize the standard score which we can get. Second, the model includes a goal programming concept which minimizes the gap between the adjusting goal and the result of the adjustment. Third, the objective function of the linear programing is the weighted sum of the score distortion and the number of applicants. Fourth, the model is applied to the score adjustment problem for the whole 11 optional subjects of the social science test section. The suggested linear programming model is a generalization of the multi-tests linking problem. So, the approach is consistent with the measurement theory for the two tests and can be applied to the optional three or more tests which do not have a common anchor test or a common anchor group. The college admission decision with CSAT score can be improved by using the suggested linear programming model.

• 대한기계학회논문집A
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• 제21권11호
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• pp.1765-1772
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• 1997

The layered composites have a character to change of structure stiffness with respect to the layup angles. The deformations in the fan-blades to be initially designed by considering efficiency and noise, etc., which arise due to the pressure during the fan operation, can make the fan inefficient. Thus, so as to minimize the deformations of the blades, it is needed to increase the stiffness of the blades. An investigation has been performed to develop the three dimensional layered composite shell element with the drilling degree of freedom and the optimization module for finding optimal layup angles with sensitivity analysis. And then they have been verified. In this study, the analysis model is engine cooling fan of automobile. In order to analyzes the stiffness of the composite fan blades, finite element analysis is performed. In addition, it is linked with optimal design process, and then the optimal angles that can maximize the stiffness of the blades are found. In the optimal design process, the deformations of the blades are considered as multiobjective functions, and this results minimum bending and twisting simultaneously.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.