• Industrial Engineering and Management Systems
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• 제11권3호
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• pp.215-223
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• 2012

This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

• 설비공학논문집
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• 제16권3호
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• pp.293-302
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• 2004

In this study the optimization of plate-fin type heat sink for the thermal stability is peformed numerically. The optimum design variables are obtained when the temperature rise and the pressure drop are minimized simultaneously. The flow and thermal fields are predicted using the finite volume method and the optimization is carried out by using the sequential quadratic programming (SQP) method which is widely used in the constrained non-linear optimization problem. The results show that when the temperature rise is less than 34.6K, the optimal design variables are as follows; B$_1$=2.468mm, B$_2$=1.365mm, and t=10.962mm. The Pareto optimal solutions are also presented for the pressure drop and the temperature rise.

• Journal of Mechanical Science and Technology
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• 제18권9호
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• pp.1590-1603
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• 2004

In this study the optimization of plate-fin type heat sink with vortex generator for the thermal stability is performed numerically. The optimum solutions in the heat sink are obtained when the temperature rise and the pressure drop are minimized simultaneously. Thermal performance of heat sink is influenced by the heat sink shape such as the base-part fin width, lower-part fin width, and basement thickness. To acquire the optimal design variables automatically, CFD and mathematical optimization are integrated. The flow and thermal fields are predicted using the finite volume method. The optimization is carried out by means of the sequential quadratic programming (SQP) method which is widely used for the constrained nonlinear optimization problem. The results show that the optimal design variables are as follows; B$_1$=2.584 mm, B$_2$=1.741 mm, and t=7.914 mm when the temperature rise is less than 40 K. Comparing with the initial design, the temperature rise is reduced by 4.2 K, while the pressure drop is increased by 9.43 Pa. The relationship between the pressure drop and the temperature rise is also presented to select the heat sink shape for the designers.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• 한국항공우주학회지
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• 제34권5호
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• pp.56-64
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• 2006

정지궤도 위성의 상대위치보정은 제한된 위지보정 박스내에 다수의 위성을 운용함으로서 발생하는 위성간 충돌, 전파간섭, 가림현상 등을 해결하기 위해 반드시 필요한 기술이다. 본 논문에서는 정지궤도 위성의 상대 궤도 운동 분석과 최적화 기법에 근거한 문제의 정립으로 상대위치보정을 수행하였다. 이상적인 정지궤도에 대한 상대운동을 다수의 멱함수와 주기함수로 표현하고, 상대위치보정에 필요한 조건들을 이들 함수로 표현한다. 이러한 구속 조건식과 더불어 연료 최소화 같은 가격 함수를 최소화하는 과정에서 궤도 수정에 필요한 절차를 수립하게 된다. 비선형 시뮬레이션을 통하여 본 논문에서 제시하고 있는 절차의 타당성을 검증하였고 또한 기존의 고전적인 방법과 비교하였다.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.436-445
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• 2013

The use of a demand response controller is necessary for electric devices to effectively respond to time varying price signals and to achieve the benefits of cost reduction. This paper describes a new formulation with the form of constrained optimization for designing an optimal demand response controller. It is demonstrated that constrained optimization is a better approach for the demand response controller, in terms of the ambiguity of device operation and the practicality of implementation of the optimal control law. This paper also proposes a design scheme to construct a demand response controller that is useful when a system controller is already adapted or optimized for the system. The design separates the demand response function from the original system control function while leaving the system control law unchanged. The proposed formulation is simulated and compared to the system with simple dynamics. The effects of the constraints, the system characteristics and the electricity price are examined further.

• 대한교통학회지
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• 제26권5호
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• pp.175-184
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• 2008

도로용량 증대를 위한 소요예산 추정문제는 관련주체인 이용자와 공급자의 입장을 모두 반영할 필요가 있다. 본 연구에서는 총통행시간, 형평성, 환경비용을 평가지표로 설정하고, 3가지 평가지표에 대한 관련주체의 요구사항이 만족되는 대안 중 소요예산을 최소화하는 최적 도로용량 증대 대안을 선정하는 문제를 모형화하였다. 일반적으로 도로용량 증대를 위한 소요예산 추정문제는 Network Design Problem(NDP)로 다루어지며, 이용자와 공급자의 다른 입장을 고려하기 위해 Bi-level 최적화문제로 모형화된다. 본 연구에서는 장래 교통수요의 불확실성을 반영하기 위해 확률모형(Stochastic model)을 적용하고, 평가지표별 신뢰도를 차별화하기 위해 Chance-constrained model(CCM)를 적용하였으며, 3가지 평가지표의 제약식을 만족하면서 소요예산을 최소화하는 목적함수를 만족하는 최적대안을 선정하기 위해 렉시코그라픽(Lexicographic) 최적화문제로 접근하였다. 예제 네트워크를 통하여 분석한 결과, 평가지표별 신뢰도 및 교통수요 변화율이 클수록 더욱 많은 소요예산이 요구되며, 평가지표별 신뢰도가 클수록 장래 교통수요의 변화에 더욱 탄력적으로 대응할 수 있는 대안이 선정되었다. 제안된 모델은 다양한 관련주체의 입장을 모두 고려한 최적 도로용량 증대 대안과 소요예산을 선정함과 동시에, 도로용량 증대량의 변화에 따른 평가지표간 상쇄관계(Tradeoff)와 도로 네트워크 개선을 위한 예산 배분의 포트폴리오를 정책결정자에게 제공 가능하다.

• 산업경영시스템학회지
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• 제43권2호
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• pp.79-86
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• 2020

To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.

• 대한의용생체공학회:의공학회지
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• 제30권5호
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• pp.373-380
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• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• Journal of Mechanical Science and Technology
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• 제20권3호
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• pp.366-375
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• 2006

In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps: the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.