• Title/Summary/Keyword: Arrival Process

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The BMAP/G/1Queue with Correlated Flows of Customers and Disasters

  • Kim, Che-Soong
    • Journal of Korea Society of Industrial Information Systems
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    • v.10 no.2
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    • pp.42-47
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    • 2005
  • A single-server queueing model with the Batch Markovian Arrival Process and disaster ow correlated with the arrival process is analyzed. The numerically stable algorithm for calculating the steady state distribution of the system is presented.

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A Simulation Study on the Variability Function of the Arrival Process in Queueing Networks (시뮬레이션을 이용한 대기행렬 네트워크 도착과정의 변동성함수에 관한 연구)

  • Kim, Sun-Kyo
    • Journal of the Korea Society for Simulation
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    • v.20 no.2
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    • pp.1-10
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    • 2011
  • In queueing network analysis, arrival processes are usually modeled as renewal processes by matching mean and variance. The renewal approximation simplifies the analysis and provides reasonably good estimate for the performance measures of the queueing systems under moderate conditions. However, high variability in arrival process or in service process requires more sophisticated approximation procedures for the variability parameter of departure/arrival processes. In this paper, we propose an heuristic approach to refine Whitt's variability function with the k-interval squared coefficient of variation also known as the index of dispersion for intervals(IDI). Regression analysis is used to establish an empirical relationships between the IDI of arrival process and the IDI of departure process of a queueing system.

Application of GTH-like algorithm to Markov modulated Brownian motion with jumps

  • Hong, Sung-Chul;Ahn, Soohan
    • Communications for Statistical Applications and Methods
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    • v.28 no.5
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    • pp.477-491
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    • 2021
  • The Markov modulated Brownian motion is a substantial generalization of the classical Brownian Motion. On the other hand, the Markovian arrival process (MAP) is a point process whose family is dense for any stochastic point process and is used to approximate complex stochastic counting processes. In this paper, we consider a superposition of the Markov modulated Brownian motion (MMBM) and the Markovian arrival process of jumps which are distributed as the bilateral ph-type distribution, the class of which is also dense in the space of distribution functions defined on the whole real line. In the model, we assume that the inter-arrival times of the MAP depend on the underlying Markov process of the MMBM. One of the subjects of this paper is introducing how to obtain the first passage probabilities of the superposed process using a stochastic doubling algorithm designed for getting the minimal solution of a nonsymmetric algebraic Riccatti equation. The other is to provide eigenvalue and eigenvector results on the superposed process to make it possible to apply the GTH-like algorithm, which improves the accuracy of the doubling algorithm.

MAP/G/1/K QUEUE WITH MULTIPLE THRESHOLDS ON BUFFER

  • Choi, Doo-Il
    • Communications of the Korean Mathematical Society
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    • v.14 no.3
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    • pp.611-625
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    • 1999
  • We consider ΜΑΡ/G/ 1 finite capacity queue with mul-tiple thresholds on buffer. The arrival of customers follows a Markov-ian arrival process(MAP). The service time of a customer depends on the queue length at service initiation of the customer. By using the embeded Markov chain method and the supplementary variable method, we obtain the queue length distribution ar departure epochs and at arbitrary epochs. This gives the loss probability and the mean waiting time by Little's law. We also give a simple numerical examples to apply the overload control in packetized networks.

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Waiting Times in Polling Systems with Markov-Modulated Poisson Process Arrival

  • Kim, D. W.;W. Ryu;K. P. Jun;Park, B. U.;H. D. Bae
    • Journal of the Korean Statistical Society
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    • v.26 no.3
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    • pp.355-363
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    • 1997
  • In queueing theory, polling systems have been widely studied as a way of serving several stations in cyclic order. In this paper we consider Markov-modulated Poisson process which is useful for approximating a superposition of heterogeneous arrivals. We derive the mean waiting time of each station in a polling system where the arrival process is modeled by a Markov-modulated Poisson process.

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Predicting the Score of a Soccer Match by Use of a Markovian Arrival Process (마코비안 도착과정을 이용한 축구경기 득점결과의 예측)

  • Kim, Nam-Ki;Park, Hyun-Min
    • IE interfaces
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    • v.24 no.4
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    • pp.323-329
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    • 2011
  • We develop a stochastic model to predict the score of a soccer match. We describe the scoring process of the soccer match as a markovian arrival process (MAP). To do this, we define a two-state underlying Markov chain, in which the two states represent the offense and defense states of the two teams to play. Then, we derive the probability vector generating function of the final scores. Numerically inverting this generating function, we obtain the desired probability distribution of the scores. Sample numerical examples are given at the end to demonstrate how to utilize this result to predict the final score of the match.

Performance Evaluation of the WiMAX Network Based on Combining the 2D Markov Chain and MMPP Traffic Model

  • Saha, Tonmoy;Shufean, Md. Abu;Alam, Mahbubul;Islam, Md. Imdadul
    • Journal of Information Processing Systems
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    • v.7 no.4
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    • pp.653-678
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    • 2011
  • WiMAX is intended for fourth generation wireless mobile communications where a group of users are provided with a connection and a fixed length queue. In present literature traffic of such network is analyzed based on the generator matrix of the Markov Arrival Process (MAP). In this paper a simple analytical technique of the two dimensional Markov chain is used to obtain the trajectory of the congestion of the network as a function of a traffic parameter. Finally, a two state phase dependent arrival process is considered to evaluate probability states. The entire analysis is kept independent of modulation and coding schemes.

Development of Stochastic Model and Simulation for Spatial Process Using Remotely Sensed Data : Fire Arrival Process (원격탐사자료를 이용한 공간적 현상의 모형화 및 시뮬레이션 : 자연화재발생의 경우)

  • 정명희
    • Spatial Information Research
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    • v.6 no.1
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    • pp.77-90
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    • 1998
  • The complex interactions of climate, topography, geology, biota and hwnan activities result in the land cover patterns, which are impacted by natural disturbances such as fire, earthquake and flood. Natural disturbances disrupt ecosystem communities and change the physical environment, thereby generating a new landscape. Community ecologists believe that disturbance is critical in determining how diverse ecological systems function. Fires were once a major agent of disturbance in the North American tall grass prairies, African savannas, and Australian bush. The major focus of this research was to develop stochastic model of spatial process of disturbance or spatial events and simulate the process based on the developed model and it was applied to the fire arrival process in the Great Victoria Desert of Australia, where wildfires generate a mosaic of patches of habitat at various stages of post-fire succession. For this research, Landsat Multi-Spectral Scanner(MSS) data covering the period from 1972 to 1994 were utilized. Fire arrival process is characterized as a spatial point pattern irregularly distributed within a region of space. Here, nonhomogeneous planar Poisson process is proposed as a model for the fire arrival process and rejection sampling thinning the homogeneous Poisson process is used for its simulation.

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RUIN PROBABILITIES IN THE RISK MODEL WITH TWO COMPOUND BINOMIAL PROCESSES

  • Zhang, Mao-Jun;Nan, Jiang-Xia;Wang, Sen
    • Journal of applied mathematics & informatics
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    • v.26 no.1_2
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    • pp.191-201
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    • 2008
  • In this paper, we consider an insurance risk model governed by a compound Binomial arrival claim process and by a compound Binomial arrival premium process. Some formulas for the probabilities of ruin and the distribution of ruin time are given, we also prove the integral equation of the ultimate ruin probability and obtain the Lundberg inequality by the discrete martingale approach.

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