• 한국지반공학회논문집
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• 제35권4호
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• pp.27-35
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• 2019

역해석 수행 시 상대적으로 복잡한 공간 및 목표 설계 변수가 많은 경우, 지반공학 분야에 적용하기 위한 연구를 수행하였다. 지반공학 다변수 문제에 대한 모델로 터널 분야 및 흙막이벽체에 대해서 Sharan 공식 및 Blum 방법을 사용하였다. 최적화 방법은 크게 결정론적인 방법 및 확률론적인 방법으로 구분된다. 본 연구에서는 전자 중 모의강화법(SA), 후자 중 차분진화 알고리즘(DEA), 입자 군집 최적화 알고리즘(PSO)을 선택하여 다변수 모델을 적용해서 비교하였다. 지반공학 다변수 역해석 문제에서 결정론적인 방법은 문제가 있음을 확인하였고, 차분진화 알고리즘의 우수성을 확인하였다. DEA는 Sharan의 이론 해에 대한 문제에서 평균 3.12%, Blum 문제에 대해서 평균 2.23% 오차율을 보였고, 반복 탐색 회수도 가장 작은 것으로 파악되었다. DEA 대비해서 SA는 117.39~167.13배, PSO는 2.43~6.91배의 탐색시간이 소요되었다. 지반공학 문제의 다변수 역해석에 차분진화 알고리즘을 적용하면, 계산속도 및 정확도가 향상될 것으로 기대된다.

• 한국시뮬레이션학회논문지
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• 제8권3호
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• pp.1-16
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• 1999

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event system. The proposed algorithm in this paper searches the effective and reliable alternatives satisfying the target values of the system to be designed through a single run in a relatively short time period. It tries to estimate an autoregressive model, and construct mean and confidence interval for evaluating correctly the objective function obtained by small amount of output data. The experimental results using the proposed method are also shown.

• 대한산업공학회지
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• 제12권1호
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• pp.107-118
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• 1986

A heuristic algorithm (SIMICOM) has been designed and tested for optimizing simulated stochastic systems whose performances are functions of several discrete decision variables. The approach adopted utilizes an integer complex method coupled with techniques of establishing confidence intervals for the system's responses. It can handle a general class of optimization problems that could be constrained or unconstrained. In constrained cases, the constraints could either be explicit analytical functions of decision variables or be expressed as other responses of the simulation model. In addition to obtain a reasonably accurate solution, the economic aspect of obtaining the solution has also been taken into consideration.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1992년도 춘계공동학술대회 발표논문 및 초록집; 울산대학교, 울산; 01월 02일 May 1992
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• pp.117-126
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• 1992

This study proposes a new interactive multicriteria method for determining the best levels of the decision variables needed to optimize a stochastic computer simulation with multiple response variables. The method, called the Pairwise Comparison Stochastic Cutting Plane (PCSCP) method, combines good features from interactive multiple objective mathematical programming methods and response surface methodology. The major characteristics of the PCSCP algorithm are: (1) it interacts progressively with the decision maker (DM) to obtain his preferences, (2) it uses good experimental design to adequately explore the decision space while reducing the burden on the DM, and (3) it uses the preference information provided by the DM and the sampling error in the responses to reduce the decision space. This paper presents the basic concepts of the PCSCP method along with its performance for solving randomly selected test problems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 정기총회 및 창립40주년기념 학술대회 학회본부
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• pp.106-108
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• 1987

Until now, inflow has been handled an independent log-normal random variable in the problem of planning the long-term operation of a multi-reservoir hydrothermal electric power generation system. This paper introduces the detail study for making rule curve by applying weekly time interval for handling inflows. The hydro system model consists of a set of reservoirs and ponds. Thermal units are modeld by one equivalent thermal unit. Objective is minimizing the total cost that the summation of the fuel cost of equivalent thermal unit at each time interval. For optimization, stochastic dynamic programming(SDP) algorithm using successive approximations is used.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1064-1072
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• 2017

The power of controlled generators in microgrids randomly fluctuate because of the stochastic volatility of the outputs of photovoltaic systems and wind turbines as well as the load demands. To address and dispatch these stochastic factors for daily operations, a dynamic economic dispatch model with the goal of minimizing the generation cost is established via chance-constrained programming. A Monte Carlo simulation combined with particle swarm optimization algorithm is employed to optimize the model. The simulation results show that both the objective function and constraint condition have been tightened and that the operation costs have increased. A higher stability of the system corresponds to the higher operation costs of controlled generators. These operation costs also increase along with the confidence levels for the objective function and constraints.

• Nuclear Engineering and Technology
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• 제29권1호
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• pp.68-77
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• 1997

The reactor power control system is described in the fashion of the order increased LQR system. To obtain the optimal state feedback gain vectors, the weighting matrix of the performance function should be determined. Since the contentional method has some limitations, stochastic searching methods are investigated to optimize the LQR weighting matrix using the modified genetic algorithm combined with the simulated annealing, a new optimizing tool named the hybrid MGA-SA is developed to determine the weighting parameters of the LQR system. This optimizing tool provides a more systematic approach in designing the LQR system. Since it can be easily incorporated with any forms of the cost function, it also provides the great flexibility in the optimization problems.

• 산업경영시스템학회지
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• 제40권4호
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• 정보과학회 논문지
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• 제44권7호
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• pp.726-732
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• 2017

무선 센서 네트워크의 분산 센싱 및 예측에 대한 실제 Application에서 네트워크 환경 센싱 기능은 움직이는 소스 신호의 잡음 및 많은 센싱 정보들 때문에 매우 동적인 기능을 요구한다. 최근의 Distributed Online Convex Optimization 프레임워크는 분산된 방식으로 센서 네트워크를 통해 확률적인 학습 문제를 해결하기 위한 유망한 접근법으로 개발되었다. 기존의 Distributed Saddle Point Algorithm (DSPA)의 학습 결과에서 수렴 속도와 안정성은 이동성의 영향을 받을 수 있다. 이에 본 논문에서는 움직이는 소스 신호 시나리오의 동시 검출에서 예측을 안정화하고 보다 나은 수렵 속도를 달성하기 위해 통합 Sliding Windows 메커니즘을 제안한다.

• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1245-1255
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• 2017

온라인 학습은 자료가 실시간으로 혹은 배치 단위로 축적되는 상황에서 주어진 목적함수의 해를 계산하는 방법을 말한다. 온라인 학습 알고리즘 중 배치를 이용한 확률적 경사 하강법 (stochastic gradient decent method)은 가장 많이 사용되는 방법 중 하나다. 이 방법은 구현이 쉬울 뿐만 아니라 자료가 동질적인 분포를 따른다는 가정 하에서 그 해의 성질이 잘 연구되어 있다. 하지만 자료에 특이값이 있거나 임의의 배치가 확률적으로 이질적 성질을 가질 때, 확률적 경사 하강법이 주는 해는 큰 편이를 가질 수 있다. 본 연구에서는 이러한 비정상 배치 (abnormal batch) 있는 자료 하에서 효과적으로 온라인 학습을 수행할 수 있는 수정된 경사 하강 알고리즘 (modified gradient decent algorithm)을 제안하고, 그 알고리즘을 통해 계산된 해의 수렴성을 밝혔다. 뿐만 아니라 간단한 모의실험을 통해 제안한 방법의 이론적 성질을 실증하였다.