• 대한토목학회논문집
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• 제14권6호
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• pp.1375-1383
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• 1994

관수로시스템 문제는 수리학적 및 시스템운영 제약조건아래서 시스템의 전체비용을 최소비용으로 구하는 것이다. 관수로시스템 문제는 수많은 국지해(local minimum)을 갖는 비볼록(nonconvex) 이므로 종래의 최적화 기법은 임의의 국지해만을 구할 수 있다. 따라서 본 연구에서는 좀더 나은 국지해를 구하기 위해 외부탐사 및 내부최적화 단계 즉 2 단계 분해기법을 제안하였다. 외부탐사 단계에서는 관로들의 최적유량을 찾기 위해 여러 국지해 사이를 이동하면서 좀더 나은 국지해를 찾는 방법인 추계학적탐사방법(stochastic probing method)을 이용 하였고 내부최적화 단계(local minimizer)에서는 외부탐사 단계에서 구한 국지해를 증진시킨다. 이 제안한 방법은 신설 관수로시스템 설계와 기존 관수로시스템의 확장에 적용할 수 있으며, 제안한 방법의 효율성을 검증하기 위해 어느 관수로시스템을 표본으로 채택하여 제안한 방법을 적용한 결과 먼저 발표된 연구자들의 결과보다 적은 비용으로 설계할 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제5권2호
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• 한국강구조학회 논문집
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• 제17권6호통권79호
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• pp.661-672
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• 2005

본 연구에서는 많은 양의 함수 계산을 요구하는 확률론적 최적화 기법을 보다 효과적으로 강구조물에 적용하여 수행하고자 한다. 다양한 과학, 응용공학 분야에서 많은 시간이 소요되는 과정을 대체하는데 효과적인 도구로 출현한 인공신경망을 최적화 과정 중 많은 수의 유한요소 해석이 요구되는 재해석 문제에 적용함으로서 유한요소법의 평형방정식의 해의 근사해를 추정하여 재해석과정을 보다 간단하고 용이하게 수행하고자 한다. 또한 이용된 인공신경망의 학습효과의 개선을 위해 유전알고리즘을 적용한다. 확률론적 구조최적화 기법으로는 진화론적 방법에 기초한 알고리즘을 사용한다. 수치 예로써 전형적인 체적(중량)문제와 실 경비함수를 목적함수로 갖는 강구조물 모형에 본 연구의 알고리즘을 적용하여 본 알고리즘의 적용성과 타당성을 증명하였다.

• 산업경영시스템학회지
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• 제39권4호
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• pp.137-146
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• 2016

In recent years, business environment is faced with multi uncertainty that have not been suffered in the past. As supply chain is getting expanded and longer, the flow of information, material and production is also being complicated. It is well known that development service industry using application software has various uncertainty in random events such as supply and demand fluctuation of developer's capcity, project effective date after winning a contract, manpower cost (or revenue), subcontract cost (or purchase), and overrun due to developer's skill-level. This study intends to social contribution through attempts to optimize enterprise's goal by supply chain management platform to balance demand and supply and stochastic programming which is basically applied in order to solve uncertainty considering economical and operational risk at solution supplier. In Particular, this study emphasizes to determine allocation of internal and external manpower of developers using S&OP (Sales & Operations Planning) as monthly resource input has constraint on resource's capability that shared in industry or task. This study is to verify how Stochastic Programming such as Markowitz's MV (Mean Variance) model or 2-Stage Recourse Model is flexible and efficient than Deterministic Programming in software enterprise field by experiment with process and data from service industry which is manufacturing software and performing projects. In addition, this study is also to analysis how profit and labor input plan according to scope of uncertainty is changed based on Pareto Optimal, then lastly it is to enumerate limitation of the study extracted drawback which can be happened in real business environment and to contribute direction in future research considering another applicable methodology.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2009년도 추계학술발표논문집
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• pp.715-718
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• 2009

The assembly line is one of the typical process hard to analyze with mathematical methods including even stochastic approaches, because it includes many manual operations varying drastically depending on operators' skills. In this paper, we suggest the simulation optimization method to design the optimal assembly line of a gas oven. To achieve the optimal design, firstly, we modeled the real gas oven assembly line with actual data, such as assembly procedures, operation rules, and other input parameters and so on. Secondly, we build some alternatives to enhance the line performance based on business rules and other parameters. The DOE(Design Of Experiment) techniques were used for testing alternatives under various situations. Each alternatives performed optimization process with evolution strategy; one of the GA(Genetic Algorithm) techniques. As a result, we can make about 7% of throughputs up with the same time and cost. By this process, we expect the assembly line can obtain the solution compatible with their own problems.

• 한국국방경영분석학회지
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• 제29권2호
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• pp.100-110
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• 2003

Equipment replacement policy may not be defined with certainty, because physical states of any technological system may not be determined with foresight. This paper presents Markov Decision Process(MDP) model for army equipment which is subject to the uncertainty of deterioration and ultimately to failure. The components of the MDP model is defined as follows: ⅰ) state is identified as the age of the equipment, ⅱ) actions are classified as 'keep' and 'replace', ⅲ) cost is defined as the expected cost per unit time associated with 'keep' and 'replace' actions, ⅳ) transition probability is derived from Weibull distribution. Using the MDP model, we can determine the optimal replacement policy for an army equipment replacement problem.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 춘계학술대회 논문집
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• pp.328-333
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• 2007

Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for tm based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. So, Markov state model is utilized considering stochastic state in RCM In this paper, a Markov state model much can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.

• 한국지능시스템학회논문지
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• 제7권2호
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• pp.25-33
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• 1997

A DEDS is a system whose states change in response to the occurence of events from a predefined event set. In this paper, we consider the optimal control and reasoning problem for Discrete Event Systems(DES) in the Temporal Logic Framework(TEL) which have been recnetly defined. The TLE is enhanced with objective functions(event cost indices) and a measurement space is alos deined. A sequence of event which drive the system form a give initial state to a given final state is generated by minimizing a cost functioin index. Our research goal is the reasoning of optimal trajectory and the design of the optimal controller for DESs. This procedure could be guided by the heuristic search methods. For the heuristic search, we suggested the Stochastic Ruler algorithm, instead of the A algorithm with difficulties as following ; the uniqueness of solutions, the computational complexity and how to select a heuristic function. This SR algorithm is used for solving the optimal problem. An example is shown to illustrate our results.

• 상하수도학회지
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• 제11권3호
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• pp.87-95
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• 1997

A river water quality management model was made by Dynamic programming. This model optimizes the wastewater treatment cost of the application area, and computed water quality with it must meet the water quality standard. And this model takes into consideration tributary input, wastewater treatment plant effluent, withdrawls for several purposes. Modified Streeter-Phelps equation was used to calculate BOD and DO. Optimization problem was solved with particular exceedance probability flow, and the water quality of each point was calculated with the decided treatment efficiencies. At that time, the probability satisfying the water quality standard of constraints to the exceedance probability of the flow. The developed model was applied to the lower part of the Han-River. The reliability to meet the water quality standard is 70 % when 4 wastewater treatment plants of Seoul City are operated by activated sludge system at autumn of the year 2001. Treatment cost of this case is 121.288 billion won per year.

• 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.