• International Journal of Contents
• /
• 제5권4호
• /
• pp.88-93
• /
• 2009

It is important for communication networks to possess the capability to overcome failures and provide survivable services. We address modeling and analysis of performability affected by both performance and availability of system components for a token ring network under failure and repair conditions. Stochastic reward nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, hierarchical SRN modeling techniques are used to overcome state largeness problem. The upper level model is used to compute availability and the lower level model captures the performance. And Normalized Throughput Loss (NTL) is obtained for the composite ring network for each node failures occurrence as a performability measure. One of the key contributions of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performability analysis for a token ring network under SRN reward concepts.

• Journal of Ubiquitous Convergence Technology
• /
• 제2권2호
• /
• pp.105-110
• /
• 2008

The definition of the program control is introduced on the theory of the basis of the first integrals SDE system. That definition allows constructing the program control gives opportunity to stochastic system to remain on the given dynamic variety. The program control is considered in terms of dynamically invariant for stochastic process.

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2005년도 추계학술대회
• /
• pp.151-152
• /
• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and Stochastic Volatility Models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• Journal of the Korean Data and Information Science Society
• /
• 제16권4호
• /
• pp.835-841
• /
• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and stochastic volatility models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• 인터넷정보학회논문지
• /
• 제16권5호
• /
• pp.59-66
• /
• 2015

확률적 워크플로우 모델링 방법은 워크플로우 인텔리전스를 지원하기 위한 수학적 방법으로서 워크플로우 모델의 분석 및 시뮬레이션에 널리 사용되고 있다. 그동안에 다양한 확률적 모델링 방법이 제안되었지만, 본 논문에서는 자원관점의 모델링 방법으로서 워크플로우 기반 소셜 네트워크를 구성하는 수행자간의 업무전달 관계를 확률적으로 나타내는 모델을 제안한다. 업무전달 관계의 확률은 단위업무 사이의 제어흐름에서 발생하는 업무전이 확률과 업무와 수행자간의 할당 확률에 의해 결정된다. 이를 위해, 본 논문에서는 정보제어넷을 기반으로 확률적 워크플로우 모델과 확률적 업무전달 관계 모델을 정형적으로 정의하고, 이를 추출하기 위한 알고리즘에 대하여 설명한다. 결과적으로 제안 모델은 조직 및 자원관점의 워크플로우 시뮬레이션 및 사후 모델-로그 비교분석에 적용될 것으로 기대된다.

• 한국시뮬레이션학회:학술대회논문집
• /
• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
• /
• pp.1-2
• /
• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.

• Industrial Engineering and Management Systems
• /
• 제16권1호
• /
• pp.44-51
• /
• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• Structural Engineering and Mechanics
• /
• 제6권5호
• /
• pp.473-484
• /
• 1998

The first and second moments of response variables for SDOF systems with hysteretic nonlinearity are obtained by a direct linearization procedure. This adaptation in the implementation of well-known statistical linearization methods, provides concise, model-independent linearization coefficients that are well-suited for numerical solution. The method may be applied to systems which incorporate any hysteresis model governed by a differential constitutive equation, and may be used for zero or non-zero mean random vibration. The implementation eliminates the effort of analytically deriving specific linearization coefficients for new hysteresis models. In doing so, the procedure of stochastic analysis is made independent from the task of physical modeling of hysteretic systems. In this study, systems with three different hysteresis models are analyzed under various zero and non-zero mean Gaussian White noise inputs. Results are shown to be in agreement with previous linearization studies and Monte Carlo Simulation.

• 한국공작기계학회논문집
• /
• 제13권2호
• /
• pp.81-87
• /
• 2004

This paper deals with a new approach to tolerance optimization problems. Optimal tolerance allotment problems can be formulated as stochastic optimization problems. Most schemes to solve the stochastic optimization problems have been found to exhibit difficulties in multivariate integration of the probability density function. As a typical example of stochastic optimization the optimal tolerance allotment problem has the same difficulties. In this stochastic model, manufacturing system is represented by Gauss-Markov stochastic process and the manufacturing unit availability is characterized for realistic optimization modeling. The new algorithm performed robustly for a large deviation approximation. A significant reduction in computation time was observed compared to the results obtained in previous studies.

• 산업공학
• /
• 제9권2호
• /
• pp.191-202
• /
• 1996

A variety of approaches on performance evaluation modeling have appeared in the technical literature for flexible manufacturing systems(FMS) which can be evaluated only through computer simulation. This study represents a comparative approach for FMS performance evaluation modeling based on reliability, availability and maintainability, and life cycle cost. The methodology proposed in this research includes the following three-step generative approaches. First, a static model to find the initial system configuration is considered under the assumption that the system availability is given as one (failure and maintenance are not considered), and in second step, a stochastic simulation is proposed to serve as a performance evaluation model for FMS with stochastic failure and repair time. In the last step, we developed a simulation modeling using a simulator, FACTOR/AIM to consider a variety of performance factors and dynamic behavior of FMS. Also the applicability and validity of the proposed approaches has been tested and compared through the results of a sample problem using computer programs and procedures developed in each step.