• Korean System Dynamics Review
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• v.13 no.2
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• pp.47-71
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• 2012

The purpose of this research is the examination of validity of data as well as simulation model, i.e. to simulate the real data in the SD model with the least error using the adjustments for the faithful reflection of real data to the simulation. In general, SD programs (e.g. VENSIM) utilize the Euler or Runge-Kutta method as an algorithm. It is possible to reflect the trend of real data via these two estimation methods however can cause the validity problem in case of the simulation requiring the accuracy as they have endogenous errors. In this article, the future population estimated by the Korea National Statistical Office (KNSO) to 2050 is simulated by the aging chain model, dividing the population into three cohorts, 0-14, 15-64, 65 and over cohorts by age and offering the adjustments to them. Adjustments are calculated by optimization with three different methods, optimization in EXCEL, manual optimization with iterative calculation, and optimization in VENSIM DSS, the results are compared, and at last the optimal adjustment set with the least error are found among them. The simulation results with the pre-determined optimal adjustment set are validated by methods proposed by Barlas (1996) and other alternative methods. It is concluded that the result of simulation model in this research has no significant difference from the real data and reflects the real trend faithfully.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.95-104
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• 2017

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.

• Journal of the Korean Society for Railway
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• v.10 no.3 s.40
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• pp.306-312
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• 2007

This paper presents the optimal design method of the train repair facility based on the simulation analysis. The train is divided into the power car, motorized car and passenger car for the simulation process analysis and train repair facility is composed of each subsystems such as a blast, dry and wash workshop. In simulation analysis, we consider the critical (dependent) factors and design (independent) factors for the optimal design. Therefore, a simulation optimization uses Evolution Strategy (ES) in order to find the optimal design factors. Experimental results indicate that simulation design factors are sufficient to satisfy the conditions of dependent variables. The proposed analysis method demonstrates that simulation design factors determined by the simulation optimization are appropriate for real design factors in a real situation and the accuracy and confidence for the simulation results are increased.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.956-963
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• 2020

Dose optimization for Radioactive Occupational Personal (ROP) is an important subject in nuclear and radiation safety field. The geometric environment of a nuclear facility is complex and the work area is radioactive, so traditional navigation model and radioactive data field cannot form an effective environment model for dose assessment and dose optimization. The environment model directly affects dose assessment and indirectly affects dose optimization, this is an urgent problem needed to be solved. Therefore, this paper focuses on an environment model used for Dose Assessment and Dose Optimization (DA&DO). We designed a multi-layer radiation field coupling modeling method, and then explored the influence of the environment model to DA&DO by virtual simulation. Then, a simulation test is done, the multi-layer radiation field coupling model for nuclear facilities is demonstrated to be effective for dose assessment and dose optimization through the experiments and analysis.

• Journal of the Korea Society for Simulation
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• v.9 no.2
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• pp.89-102
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• 2000

In this paper, we consider an optimal allocation of constant service efforts in queueing network to maximize the system throughput. For this purpose, using the perturbation analysis, we apply a stochastic optimization algorithm to two types of queueing systems. Our simulation results indicate that the estimates obtained from a stochastic optimization algorithm for a two-tandem queuing network are very accurate, and those for closed loop manufacturing system are a little apart from the known optimal allocation. We find that as simulation time increases for obtaining a new gradient (performance measure with respect to decision variables) by perturbation algorithm, the estimates tend to be more stable. Thus, we consider that it would be more desirable to have more accurate sensitivity of performance measure by enlarging simulation time rather than more searching steps with less accurate sensitivity. We realize that more experiments on various types of systems are needed to identify such a relationship with regards to stopping rule, the size of moving step, and updating period for sensitivity.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.212-216
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• 1999

We propose a method for simulation optimization using stochastic genetic algorithm(GA) to get the best design and operating policies in a Direct-Input-Output Automated Manufacturing System (DIOMS).

• Journal of Korea Water Resources Association
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• v.46 no.4
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• pp.373-387
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• 2013

For the efficient operation and management of the water resources system, coordinated operation of weirs and reservoirs is required. A simulation based, and an optimization based approaches are available to deal with the operation and management problems. The simulation based approach does not guarantee an optimal solution, and the optimization based approach is not so flexible to consider, complex, nonlinear problems we will face when trying to allocate water to different uses, various demand sectors in a basin. Hence, it is important to develop a model that would compensate for the weak points in both models. We will compare and contrast intrinsic and extrinsic properties of two modeling approaches, addressing issues related to setting system operation and control rules that would lead us to more efficient use of water in the basin. As a result, we propose to use CoWMOM(Coordinated weirs and multi-reservoir operating model), a "simulation based" optimization model for a simple simulation of the past periods, and for the real-time simulation process considering uncertain inflow.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.905-908
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• 2009

In the management of groundwater in coastal areas, saltwater intrusion associated with extensive groundwater pumping, is an important problem. The groundwater optimization model is an advanced method to study the aquifer and decide the optimal pumping rates or optimal well locations. Cheng and Park gave the analytical solutions to the optimization problems basing on Strack's analytical solution. However, the analytical solutions have some limitations of the property of aquifer, boundary conditions, and so on. A simulation-optimization numerical method presented in this study can deal with non-homogenous aquifers and various complex boundary conditions. This simulation-optimization model includes the sharp interface solution which solves the same governing equation with Strack's analytical solution, therefore, the freshwater head and saltwater thickness should be in the same conditions, that can lead to the comparable results in optimal pumping rates and optimal well locations for both of the solutions. It is noticed that the analytical solutions can only be applied on the infinite domain aquifer, while it is impossible to get a numerical model with infinite domain. To compare the numerical model with the analytical solutions, calculation of the equivalent boundary flux was planted into the numerical model so that the numerical model can have the same conditions in steady state with analytical solutions.

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.1-11
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• 2003

In this paper, we focus an optimal policy focus optimal class of (s, S) inventory control systems. To this end, we use the perturbation analysis and apply a stochastic optimization algorithm to minimize the average cost over a period. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. Our simulation results indicate that the optimal estimates of s and S obtained from a stochastic optimization algorithm are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and review period. Another directions involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.