포트폴리오 구성을 위한 최적 시뮬레이션의 활용에 관한 연구

구기동 ( Ki Dong Koo ),이종구 ( Jong Gu Lee ) 한국경영공학회 2013 한국경영공학회지 Vol.18 No.2

This study was conducted for checking a developing process and a model of the Optimal Asset Allocation by the Mean-variance Model and the Optimal Simulation. Used mainly as an objective function, the Asset Allocation decides investment proportion by minimizing a risk and making a rate of return a constant. The model of Asset Allocation has developed from the Mean-variance Model, the linear model, the simulation model to the Optimal Simulation step by step. In the fist stage of combining the models, a parameter for simulation and optimization is estimated by creating input data. The second stage is that the Optimal Asset Allocation is produced by the quadratic programming. In the final step, the optimal range is calculated by the Optimal Simulation. And the optimal range is divided into the best case and the worst one. Each model draws an optimal investment proportion through an objective function. In a situation where a risk is the least, an investment proportion to a safe asset having low fluctuation appeared high independently of the model`s type. While a resource distribution is done by the Mean-variance Model, the Optimal Simulation suggested a resource distribution to risky asset under the same conditions. Therefore, it was found that there was a difference in distributing resource between the two models. It is showed that the Optimal Simulation can be used to select a concrete investment and to expect a range of a resource distribution among risky assets. The basis that chooses an asset achieving dominant result or minimizing risk by duration among individual assets can be offered. Because the domestic stock market has strong fluctuation, rather than the Mean-variance Model distributing resource conservatively, active method of resource distribution, the Optimal Simulation can make more profit. And the Optimal Simulation needs to be used actively in practical section because it has a strength the Mean-variance Model doesn`t have.

시뮬레이션 그리고 최적화 기법을 동시에 활용한 M&S 발전 방안

조남석 한국국방경영분석학회 2016 한국국방경영분석학회지 Vol.42 No.2

All the events in real life can be expressed as a model in a greater or less degree of resolution. Both Simulation and Optimization deal with the modeling system that represents a real world. However, there is a difference between them in terms of the objective for the modeling and a way looking for the solution. It is a reasonable idea to use both Simulation and Optimization to overcome its disadvantage. In this paper, we suggest two approaches for using two techniques simultaneously, especially focused on the M&S area in national defense. One method is to use Simulation model in order to find an desired optimal solution of a decision maker. It is called a Black-Box Optimization. Second method is to use Optimization model inside the Simulation model by providing a set of valuable information, mostly the optimal solution, to a decision maker. This novel idea is a very helpful when a decision maker uses a real-time simulation model.

시뮬레이션 기반의 최적화를 이용한 제조 공장의 디지털 트윈 프레임워크 개발 - 원유정제 공장의 실시간 운전 최적화 적용

고홍철,이종현,이우조 제어·로봇·시스템학회 2022 제어·로봇·시스템학회 논문지 Vol.28 No.8

A digital twin framework with Simulation-based Optimization(SBO) for industrial plants is developed. The proposed framework is composed of a combination of DWSIM and Python. DWSIM is adopted for SBO simulation because it is open source process simulation software, used to model and simulate rigorously the processes such as refinery, petrochemical, chemical, power generation and utilities. Python is adopted for SBO optimization because it provides free optimization libraries for solving various forms of nonlinear optimization problems. The combination of Aspen HYSYS and MATLAB was previously proposed for SBO and used primarily for process synthesis and design because of its advanced simulation and optimization capabilities. This study applied the SBO framework with DWSIM and Python to Crude Unit real-time optimization(RTO). The result showed that the time requirement of RTO configuration, calculation efficiency and convergence stability are acceptable compared with RTO solutions already commercialized, confirming that the SBO framework with the combination of DWSIM and Python can be helpful for Digital Twin development in industrial plants. .

시스템다이내믹스 시뮬레이션에서의 동적 최적화에 관한 연구

남광식,문성암 한국시스템다이내믹스학회 2022 한국시스템다이내믹스 연구 Vol.23 No.4

For the optimization of system dynamics simulation, only one optimal policy has been calculated through simulation by applying static optimization. This method does not reflect the reality that the policy changes depending on the point of time, so it may not be the optimal value at a specific point in time. Therefore, this study proposes a method to calculate the optimal policy for each time point by using dynamic optimization in the system dynamics simulation. For the simulation, a fixed order quantity inventory management model was built using the system dynamics software (Vensim). Dynamic optimization was limited in implementation in Vensim, so the optimal reorder point (ROP) was calculated at each time point using data analysis software (Python). This study is meaningful in that it presented a method to apply dynamic optimization in system dynamics simulation by linking Vensim and Python. It is expected that it will be used as a tool to support real-time policy making in public institutions and the private sector in the future.

Research on Simulation and Optimization of Tobacco Logistics Center Based on Flexsim

Tian Shihai,Wei Zhiqiang 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.5

According to backward technology, tedious process, failure orders and goods queuing of traditional tobacco enterprise logistics center, the process optimization methods is proposed by application of RFID technology. Firstly, actual operating environment of Harbin Tobacco Company (Abbr. HTC) logistics center in China is used as background to analyze the problems existing in the process through investigation. Secondly, the logistics center simulation model is established by using Flexsim simulation technology in order to find out the cause of the problems of logistics center operation process. Finally, operation process of logistics center is optimized. The result shows that the operating efficiency of logistics center has increased by 22.4%. It proves that the effect of this kind of research method for the optimization of tobacco logistics center process is remarkable and it provides a new way of thinking to implement RFID technology in the logistics center of this industry.

농식품 공정 최적화 시뮬레이션: 시뮬레이션 소프트웨어 적용 리뷰

서영욱(Youngwook Seo),박종률(Jong-Ryul Park),김기영(Giyoung Kim) 한국산학기술학회 2021 한국산학기술학회논문지 Vol.22 No.12

중소규모의 농식품 제조업체의 가공 및 유통 공정은 과학적이고 체계적인 기술이 도입되지 않아 제품 생산 효율이 낮은 경우가 많다. 이미 자동차, 금융, 항공, 생명공학 등 다양한 분야에서 공정 최적화, 생산성 향상을 위해 적용되어 온 시뮬레이션 기술은 최근 식품 제조공장의 생산효율을 높이기 위한 공정 최적화 연구에도 적용되기 시작하였다. 농식품 가공 및 유통 공정에 시뮬레이션 기술을 적용하면 다양한 시나리오를 가상 공간에서 시연하면서 생산 비용과 시간을 줄일 수 있는 최적의 공정을 예측할 수 있게 도와준다. 농식품 제조업체의 가공 및 유통 공정에 시뮬레이션 기술을 적용하는 연구들은 대부분 중소규모의 농식품 생산업체가 많은 개발도상국을 중심으로 수행되었으며, 곡물 저장, 제빵, 식용오일 생산 및 농산물 유통 등 다양한 분야를 대상으로 수행하였다. 이들 연구에서는 많은 경우 ARENA 등의 상용 제품을 시뮬레이션 모델 개발 및 예측 도구로 활용하였으며, CPN (colored petri net) 등의 오픈소스 기반의 소프트웨어도 다양하게 사용되고 있는 추세이다. 또한, 예측한 결과를 최적화하기 위해 마코프 결정 과정, Delmia Quest, 반응표면법을 적용하기도 했다. 농식품 제조공정에 대한 시뮬레이션 기술 적용은 추후 사물인터넷, 빅데이터 등의 정보기술을 활용하여 표준 공정 개발 및 유통 공정 최적화를 이룰 수 있을 것으로 기대된다. Small and medium-sized agri-food producers may start their business without any help from advanced technology to optimize product processing or distribution. So, they are prone to have relatively low production efficiencies. However, automobile production, banking system, the aviation industry, and biotechnology companies have already adopted simulation technology for a long time. Along these lines, the agri-food industry has also begun applying simulation technology to increase production efficiency and optimize processing. Particularly, this study introduced the research on processing optimization and proposed alternative suggestions for the agri-food processing and distribution industry, using the discrete event simulation method. Several underdeveloped countries have small and medium-sized agri-food production and processing companies that have reported the results of research on crop storage, bakery, edible oil production, and supply chain using simulation techniques. This study used commercial software such as ARENA to develop and predict an optimized procedure and advance the related research. Also, recently, open-source software such as colored Petri net (CPN) has been used as an alternative tool for modeling and optimization. This study also used the Markov decision process, Delmia Quest, and response surface method (RSM) to optimize the developed and predicted results. Finally, we note that the discrete event simulation can collaborate with information technology, the internet of things (IoT), big data, and artificial intelligence (AI) to perform a standard processing and supply chain optimization.

Multi-Objective Scheduling of Multi-Projects : A Simulation-Optimization Model

무하마드 임란,강창욱 한국산업경영시스템학회 2018 한국산업경영시스템학회 학술대회 Vol.2018 No.춘계

This paper will present a simulation-optimization model for the scheduling of multi-projects. The objectives of this research include the minimization of value added projects execution cost, project completion time, project tardiness, and underutilization of contracted or outsourced resources. It is the three-phase research. In first phase, a mathematical and simulation models will be developed for multi-objectives. In second phase simulation model will be coupled with genetic algorithm to form a simulation-optimization model. The efficiency of genetic algorithm (GA) will be improved simultaneously with fine-tuning and hybridizing with other algorithms. The third phase will involve the presentation of a numerical example for the real time application of proposed research. Solution of numerical obtained with fine-tuned and hybridized simulation integrated GA will be compared with already available methods of simulation-optimization. This research will be useful for the scheduling of projects to achieve the befits of high profit, effective resource utilization, and customer satisfaction with on time delivery of projects.

The Effect of Sample and Particle Sizes in Discrete Particle Swarm Optimization for Simulation-based Optimization Problems

Dong-Soon Yim(임동순) 한국산업경영시스템학회 2017 한국산업경영시스템학회지 Vol.40 No.1

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

시뮬레이션을 이용한 고속철도 차량기지 설계 최적화

오정헌(Jeong Heon Oh),엄인섭(In Sup Um),박춘수(Choon Soo Park),이희성(Hi Sung Lee) 대한기계학회 2012 대한기계학회 춘추학술대회 Vol.2012 No.11

This paper presents a simulation-based optimal design method for the design of the train depot. Because the train depot is composed of each subsystems such as car body and wheel workshop, the train is divided into car, car body and wheel for the process analysis and we design the simulation model. In simulation analysis, we consider the dependent variables and independent parameters. Therefore, the Multi Criteria Decision Making (MCDM) is used for selecting the alternatives and simulation optimization is proposed for finding the optimal design parameters of the selected alternative by using evolution strategy. The case study for the above design process is applied to the KTX-Ⅱ(High-Speed Railway) Train Depot. This paper provides a general design and analysis framework for the train depot design with using the simulation.

Simulation and optimization of ethanol amine production plant

Gholamreza Zahedi,Saeideh Amraei,Mazda Biglari 한국화학공학회 2009 Korean Journal of Chemical Engineering Vol.26 No.6

An industrial Ethanol Amine (EA) production plant was simulated and optimized. Due to lack of accurate reaction rate information, the first step involved obtaining reliable kinetic data from the SRI (Stanford Research Institute) industrial database and calculation using error minimization method. In the next step, by implementing the obtained reaction kinetics the whole plant was simulated using Hysys software. Simulation results were compared with the SRI data and showed that there is acceptable agreement between simulation and the measured industrial data. In the next step of study by applying the gradient search (GS) optimization technique the plant was optimized using: feeding ammonia to ethylene oxide (EO) molar ratio, water flow rate in the feed stream, and reactor temperature as optimization variables. Employing process profit as objective function the optimal operating conditions were found to be: ammonia to EO ratio of 5 (mol/mol), water flow rate of 52.59 kg mol/hr and reactor temperature of 85 ℃.