• Journal of Ship and Ocean Technology
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• 제8권2호
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• pp.41-60
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• 2004

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.

• 한국경영과학회지
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• 제18권3호
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• pp.13-34
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• 1993

By operating on many part of a software system concurrently, the parallel processing computers may provide several orders of magnitude more computing power than traditional serial computers. If the Lagrangean approximation procedure is applied to a large scale manufacturing problem which is decomposable into many subproblems, the procedure is a perfect candidate for parallel processing. By distributing Lagrangean subproblems for given multiplier to multiple processors, concurrently running processors and modifying Lagrangean multipliers at the end of each iteration of a subgradient method,a parallel processing of a Lagrangean approximation procedure may provide a significant speedup. This purpose of this research is to investigate the potential of the parallelized Lagrangean approximation procedure (PLAP) for certain combinational optimization problems in manufacturing systems. The framework of a Plap is proposed for some combinatorial manufacturing problems which are decomposable into well-structured subproblems. The synchronous PLAP for the multistage dynamic lot-sizing problem is implemented on a parallel computer Alliant FX/4 and its computational experience is reported as a promising application of vector-concurrent computing.

• 산업경영시스템학회지
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• 제37권3호
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• pp.64-74
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• 2014

We show how a supplier can peg cost measures to the reliability of his time guarantees via the penalty costs considered in the framework. The framework also enables us to study the connections between the logistics network and the market. In this context, we show that even when the market base increases significantly, the supplier can still use the logistics network designed to satisfy lower demand density, with only a marginal reduction in profit. Finally we show how the framework is useful to evaluate and compare various logistics system improvement strategies. The supplier can then easily choose the improvement strategy that increases his profit with the minimal increase in his logistics costs.

• Smart Structures and Systems
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• 제16권1호
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• 한국경영과학회지
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• 제28권4호
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• pp.191-198
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• 2003

The generalized knapsack problem or gknap is the combinatorial optimization problem of optimizing a nonnegative linear function over the integral hull of the intersection of a polynomially separable 0-1 polytope and a knapsack constraint. The knapsack, the restricted shortest path, and the constrained spanning tree problem are a partial list of gknap. More interesting1y, all the problem that are known to have a fully polynomial approximation scheme, or FPTAS are gknap. We establish some necessary and sufficient conditions for a gknap to admit an FPTAS. To do so, we recapture the standard scaling and approximate binary search techniques in the framework of gknap. This also enables us to find a weaker sufficient condition than the strong NP-hardness that a gknap does not have an FPTAS. Finally, we apply the conditions to explore the fully polynomial approximability of the constrained spanning problem whose fully polynomial approximability is still open.

• 정보과학회 논문지
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• 제44권7호
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• pp.719-725
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• 2017

약 네트워크의 관리 비용을 줄이고 성능을 향상시키기 위해 ETSI(European Telecommunication Standards Institute)는 클라우드 데이터 센터에서 네트워크 기능(Network Function)을 소프트웨어 형태로 구현할 수 있는 네트워크 기능 가상화(Network Function Virtualization) 개념을 도입했다. 네트워크 기능 가상화 구조 내에서 네트워크 기능을 물리적 노드(예: 범용 서버)에 네트워크 기능을 호스팅하여 실제 리소스를 공유할 수 있다. 네트워크 기능 가상화를 지원하는 네트워크 서비스 제공 업체의 경우, 효율적인 자원 할당 방법을 통해 운영비용(OPEX) 및 자본 비용(CAPEX)를 줄일 수 있다. 이에 본 논문에서는 최적화 방법을 통해 Network Service Chain Embedding 문제를 분석하고 Markov Approximation 프레임워크 기반 최적의 솔루션을 제안한다. 제안사항에 대한 시뮬레이션 결과는 평균 CPU 사용률이 73%, 링크 사용률이 최대 53% 증가함을 보여준다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2003년도 추계학술대회 및 정기총회
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• pp.93-96
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• 2003

The generalized knapsack problem, or gknap is the combinatorial optimization problem of optimizing a nonnegative linear functional over the integral hull of the intersection of a polynomially separable 0 - 1 polytope and a knapsack constraint. Among many potential applications, the knapsack, the restricted shortest path, and the restricted spanning tree problem are such examples. We establish some necessary and sufficient conditions for a gknap to admit a fully polynomial approximation scheme, or FPTAS, To do so, we recapture the scaling and approximate binary search techniques in the framework of gknap. This also enables us to find a condition that a gknap does not have an FP-TAS. This condition is more general than the strong NP-hardness.

• 산업경영시스템학회지
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• 제32권4호
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• pp.184-191
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• 2009

In a technological driven environment, a depreciation estimate which is based on traditional life analysis results in a decelerated rate of capital recovery. This time pattern of technological growths models needs to be incorporated into life analysis framework especially in those industries experiencing fast technological changes. The approximation technique for calculating the variance can be applied to the six growth models that were selected by the degree of skewness and the transformation of the functions. For the Pearl growth model, the Gompertz growth model, and the Weibull growth model, the errors have zero mean and a constant variance over time. However, transformed models like the linearized Fisher-Pry model, the linearized Gompertz growth model, and the linearized Weibull growth model have increasing variance from zero to that point at which inflection occurs. It can be recommended that if the variance of error over time is increasing, then a transformation of observed data is appropriate.

• 정보처리학회논문지B
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• 제11B권2호
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• pp.207-212
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• 2004

본 논문은 주식 매매 시스템을 위한 강화 학습 구조를 제시한다. 매매 시스템에 사용되는 매개변수들은 Q-학습 알고리즘에 의하여 최적화되고, 인공 신경망이 값의 근사치를 구하기 위하여 활용된다 이 구조에서는 서로 유기적으로 협업하는 다중 에이전트를 이용하여 전역적인 추세 예측과 부분적인 매매 전략을 통합하여 개선된 매매 성능을 가능하게 한다. 에이전트들은 서로 통신하여 훈련 에피소드와 학습된 정책을 서로 공유하는데, 이 때 전통적인 Q-학습의 모든 골격을 유지한다. 실험을 통하여, KOSPI 200에서는 제안된 구조에 기반 한 매매 시스템을 통하여 시장 평균 수익률을 상회하며 동시에 상당한 이익을 창출하는 것을 확인하였다. 게다가 위험 관리의 측면에서도 본 시스템은 교사 학습(supervised teaming)에 의하여 훈련된 시스템에 비하여 더 뛰어난 성능을 보여주었다.

• 여성건강간호학회지
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• 제25권1호
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• pp.31-45
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• 2019

Purpose: This study was designed to develop a valid and reliable scale for the evaluation of preconception health behavior in women preparing for pregnancy. Methods: The initial strategy included a literature review, interviews, and construction of a conceptual framework. The preliminary items were evaluated twice for content validity by experts, and modified two preliminary investigations. Participants in the 2 main investigations and the confirmation investigation were tested for reliability and validity of the preliminary scale in women preparing for pregnancy. The data were analyzed for different items exploratory and confirmatory factors. Results: The 5-point Likert scale consisted of 6 factors and 27 items. The 6-factors included 'hazardous substance factor,' 'medical management factor,' 'rest and sleep factor,' 'stress management factor,' 'information acquisition factor,' and 'resource preparation factor.' Goodness of fit of the final research model was very appropriate and based on the following measures: Q=1.98, comparative fit index=.91, Tucker-lewis index=.89, standardized root mean square residual=.07, and root mean square error of approximation=.07. The criterion validity was .64. The reliability coefficient was .92 and the test-retest reliability was .61. Conclusion: The study findings indicate that the scale can be used for the development of nursing interventions to promote preconception health behavior in women preparing for pregnancy.