Bernoulli抵消策略的负顾客的M/G/1排队系统

负顾客的排队模型是排队论近年来的新兴分支。文中引入了带有B ernou lli抵消策略的负顾客的M/G/1排队模型,即到达的负顾客以概率p抵消队首正在接受服务的正顾客,以1-p的概率抵消队尾的正顾客,利用补充变量法,状态转移法和L变换,最后得到系统稳态队长的概率母函数。     

混合排队规则的战术装备维修保障系统分析

首先将战术装备维修保障过程描述为M/M/c/k混合规则的排队过程,其损坏装备到达服从相互独立的泊松分布,维修时间服从相互独立的指数分布。同时考虑系统的到达率和维修率随系统中装备数量的变化,重要战损装备等待维修时的不耐烦性以及重要装备对一般装备的强占性优先权情况,结合战术装备维修保障系统的结构和规模,建立战术装备维修保障M/M/3/12排队模型。列出模型的平衡方程,采用矩阵的分析方法得到重要装备和一般装备的稳态分布表达式,并以队长为指标进行了系统性能的计算。     

基于N-策略的多级适应性休假的M/G/1排队系统

N-策略的休假排队模型有着广泛的应用背景。在文献[2-3]的基础上,研究了N-策略多级适应性休假M/G/1排队系统。通过使用全概率分析方法、拉普拉斯变换和拉普拉斯-斯蒂阶变换理论,得到各阶段队长的瞬态分布、队长的母函数以及平均队长的递推表达式。为预测某时刻顾客数量的概率大小、获得利益最大化的阈值N提供了理论依据。     

排队论在军事信息服务中的应用

随着信息时代战争形态和战争环境的变化,未来的军事信息服务必须为各级指挥官和作战部队提供实时、准确和完整的作战信息和战场态势.如何缩短获得信息服务的时间已成为提高系统效率的重要环节.运用排队理论,提出了改进的基于优先权的排队模型,并重点分析了系统的平均等待时间.通过与传统的排队模型相应指标的比较,改进后的模型可以有效缩短获取信息服务的时间.     

随机环境中的M/MY/1排队系统

文献［４］讨论了随机环境中的Ｍ／Ｍ／１排队模型，本文提出和讨论随机环境中的Ｍ／Ｍｙ／１排队模型，在统计平衡条件下给出了队长和等待队长的平稳分布以及平均队长和平均等待队长，得到了等待时间和逗留时间分布以及平均等待时间和平均逗留时间。     

一种基于分组传送的话音/数据综合的排队模型

本文在话音和数据均为分组传送的基础上,提出并分析了一种话音数据综合的排队模型,从M/D/1排队系统出发,推导了本模型中话音和数据分组的平均迟延及平均队长的近似公式。计算机对公式进行数值分析得出的结果和用GPSS语言模拟本排队模型得出的结果是基本吻合的。     

部分服务台同步多重休假的GI/M/c稳态队长

在经典GI/M/c排队中引入部分服务台同步多重休假策略,利用拟单生过程和矩阵几何解的方法,求解系统的稳态队长分布及其条件随机分解。     

多目标通道地空导弹武器系统抗击效率模型

针对多目标通道地空导弹武器系统的战术技术使用特点,基于地空导弹射击原理和随机服务系统理论,给出了多目标通道地空导弹武器系统抗击(服务)过程的排队模型,建立了多目标通道地空导弹武器系统抗击效率运算的数学模型,并进行了实际的算例分析。     

M/G/1(RVT,P(j))排队系统的另一分析方法及其应用

M/G/1(RVT,P(j))表示服务员具有随机长度休息时间(RVT)的、且一休息时间结束时有 j 个顾客等待的概率为 P(j)的、修正的 M/G/1 排队系统。我们用嵌入 Markov 链的技术已详细地分析过这一排队系统,这里提供另一分析方法。最后,应用这个排队系统的分析结果,对时隙 ALOHA 卫星公用信道的分组碰撞概率计算公式作了推导。     

任意寿命分布下k/N(G)系统定时维修决策模型

针对大型k/N(G)系统维修保障特点,提出了一种定时维修策略。以系统使用可用度为约束条件,以系统期望维修费用率最小化为优化目标,建立了任意寿命分布下k/N(G)系统定时维修优化模型,并提出了一种求解模型的数值迭代算法。实例分析表明,该模型既能计算定时维修策略下k/N(G)系统使用可用度和期望维修费用率,又能确定最佳的定时维修间隔期和最佳的换件维修人数,可为k/N(G)系统预防性维修提供决策支持。     

On queues with dependent interarrival and service times

AnM/G/1 queueing system is studied in which the service time required by a customer is dependent on the interarrival time between his arrival and that of his predecessor Assuming the two variables are “associated,” we prove that the expected delay in this system is less than or equal to than of a conventional M/G/1 queue This conclusion has been verified via simulation by Mitchell and Paulson [9] for a special class of dependent M/M/1 queue. Their model is a special case of the one we consider here. We also study another modified GI/G/1 queue. where the arrival process and/or the service process are individually “associated”.     

Probabilistic priorities in the M/G/1 queue

A simple method is presented for deriving the mean and variance of the queueing time distribution in an M/G/1 queue when the priorities assigned to customers have an assignment probability distribution. Several examples illustrate the results. The mean and variance of the queueing time distribution for the longest service time discipline are derived, and its disadvantages are discussed.     

Stochastic analysis of a single server retrial queue with general retrial times

Retrial queueing systems are widely used in teletraffic theory and computer and communication networks. Although there has been a rapid growth in the literature on retrial queueing systems, the research on retrial queues with nonexponential retrial times is very limited. This paper is concerned with the analytical treatment of an M/G/1 retrial queue with general retrial times. Our queueing model is different from most single server retrial queueing models in several respectives. First, customers who find the server busy are queued in the orbit in accordance with an FCFS (first‐come‐first‐served) discipline and only the customer at the head of the queue is allowed for access to the server. Besides, a retrial time begins (if applicable) only when the server completes a service rather upon a service attempt failure. We carry out an extensive analysis of the queue, including a necessary and sufficient condition for the system to be stable, the steady state distribution of the server state and the orbit length, the waiting time distribution, the busy period, and other related quantities. Finally, we study the joint distribution of the server state and the orbit length in non‐stationary regime. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 561–581, 1999     

A note on sojourn times in M/G/1 queues with instantaneous,bernoulli feedback

Queueing systems which include the possibility for a customer to return to the same server for additional service are called queueing systems with feedback. Such systems occur in computer networks for example. In these systems a chosen customer will wait in the queue, be serviced and then, with probability p, return to wait again, be serviced again and continue this process until, with probability (1 – p) = q, it departs the system never to return. The time of waiting plus service time, the nth time the customer goes through, we will call his nth sojourn time. The (random) sum of these sojourn times we will call the total sojourn time (abbreviated, sojourn time when there is no confusion which sojourn time we are talking about). In this paper we study the total sojourn time in a queueing system with feedback. We give the details for M/G/1 queues in which the decision to feedback or not is a Bernoulli process. While the details of the computations can be more difficult, the structure of the sojourn time process is unchanged for the M/G/1 queue with a more general decision process as will be shown. We assume the reader is familiar with Disney, McNickle and Simon [1].     

On a single-server queue with state-dependent service

This paper discusses a class of queueing models in which the service time of a customer al a single server facility is dependent on the queue size at the onset of its service. The Laplace transform for the wait in queue distribution is derived and the utilization of the server is given when the arrival is a homogeneous Poisson process.     

The queue G/M/m/N: Busy period and cycle,waiting time under reverse and random order service

The busy period, busy cycle, and the numbers of customers served and lost therein, of the G/M/m queue with balking is studied via the embedded Markov chain approach. It is shown that the expectations of the two discrete variables give the loss probability. For the special case G/M/1/N a closed expression in terms of contour integrals is obtained for the Laplace transform of these four variables. This yields as a byproduct the LIFO waiting time distribution for the G/M/m/N queue. The waiting time under random order service for this queue is also studied.     

A finite capacity GI/PH/1 queue with group services

We consider a finite-capacity single-server queue in which arrivals occur one at a time, according to a renewal process. The successive service times are mutually independent and have a common phase-type distribution. The customers are served in groups of size at least L, a preassigned threshold value. Explicit analytic expressions for the steady-state queue-length densities at arrivals and at arbitrary time points, and the throughput of the system are obtained. The Laplace-Stieltjes transform of the stationary waiting-time distribution of an admitted customer at points of arrivals is computed. It is shown to be of phase type when the arrival process is also of phase type. Efficient algorithmic procedures for the steady-state analysis of the model are presented. These procedures are used in arriving at an optimal value for L that minimizes the mean waiting time of an admitted customer. A conjecture on the nature of the mean waiting time is proposed.     

On the discrete-time queue length distribution under markov-dependent phases

The technique of probability generating functions has been applied to solve the steady state behavior of a discrete-time, single-channel, queueing problem wherein the arrivals to the queue at consecutive time-marks are statistically independent, but the service is accomplished in phases which are Markov-dependent. Special cases of importance have been discussed. In the end, mean number of phases, its special cases, the mean queue lengths, and the variances have been ascertained.     

State-dependent gap acceptance

This paper considers a traffic problem in which vehicles queue up according to a Poisson process on a single-lane entrance ramp prior to merging into a major stream of vehicular traffic. In order to then prevent the ramp queue from becoming too large, a model is proposed which considers a lowering of the critical gap as the ramp queue size increases. With the critical gap assumed to be a nonincreasing function of the number of vehicles on the ramp at instances that correspond to departure times of lead vehicles from the ramp queue, the resultant model is an M/G/1 queue with state-dependent service times. Some general results are obtained for this model and a specific case discussed in moderate detail.     

Approximation for the departure process of a queue in a network

A simple renewal process is identified to approximate the complex departure process of a queue often found in queueing network models. The arrival process to the queue is the superposition or merging of several independent component-renewal processes that are approximations of departure processes from other queues and external arrival processes; there is a single server with exponential service times, and the waiting space is infinite. The departure process of this queue is of interest because it is the arrival process to other queues in the network. The approximation proposed is a hybrid; the mean and variance of the approximating departure intervals is a weighted average of those determined by basic methods in Whitt [41] with the weighting function empirically determined using simulation. Tandem queueing systems with superposition arrival processes and exponential service times are used to evaluate the approximation. The departure process of the first queue in the tandem is approximated by a renewal process, the tandem system is replaced by two independent queues, and the second queue is solved analytically. When compared to simulation estimates, the average absolute error in hybrid approximations of the expected number in the second queue is 6%, a significant improvement over 22–41% in the basic methods.