空竭服务多重休假的离散时间GI/G/1重试排队系统

考虑带有空竭服务多重休假的离散时间GI/G/1重试排队系统,其中重试空间中顾客的重试时间和服务台的休假时间均服从几何分布.通过矩阵几何方法,给出了该系统的一系列性能分析指标.最终利用逼近的方法得到了部分数值结果,并通过算例说明主要的参数变化对系统人数的影响.     

有Bernoulli休假和可选服务的Geo/G/1重试排队

讨论了有Bernoulli休假策略和可选服务的离散时间Geo/G/1重试排队系统.假定一旦顾客发现服务台忙或在休假就进入重试区域,重试时间服从几何分布.顾客在进行第一阶段服务结束后可以离开系统或进一步要求可选服务.服务台在每次服务完毕后,可以进行休假,或者等待服务下一个顾客.还研究了在此模型下的马尔可夫链,并计算了在稳态条件下的系统的各种性能指标以及给出一些特例和系统的随机分解.     

清理策略下有优先权排队的M_2/G_2/1重试排队系统

在有负顾客到达可清空优先权排队中的全部顾客的机制下,研究了M_1,M_2/G_1,G_2/1重试排队系统.假设两类顾客的到达分别服从独立的泊松过程,如服务器忙,优先级高的顾客则排队等候服务,而优先级低的顾客只能进入Orbit中进行重试,直到重试成功.此外,假设负顾客的到达服从Poisson过程,当负顾客到达系统时,若发现服务台忙,将带走正在接受服务的顾客及优先权队列中的顾客.若服务台空闲,则负顾客立即消失,对系统没有任何影响.应用补充变量及母函数法给出了该模型的稳态解的拉氏变换表达式.     

带有优先权、不耐烦顾客及负顾客的$M_1,M_2/G_1,G_2/1$可修重试排队系统

研究了带有优先权,不耐烦顾客及负顾客的M1,M2/G1,G2/1可修重试排队系统.假设两类顾客的优先级不同且各自的到达过程分别服从独立的泊松过程.有优先权的顾客到达系统时如服务器忙,则以概率H1排队等候服务,以概率1-H1离开系统;而没有优先权的顾客只能一定的概率进入Orbit中进行重试,直到重试成功.此外,假设有服从Poisson过程的负顾客到达:当负顾客到达系统时,若发现服务台忙,将带走正在接受服务的顾客并使机器处于修理状态;若服务台空闲或已经处于失效状态,则负顾客立即消失,对系统没有任何影响.应用补充变量及母函数法给出了该模型的系统指标稳态解的拉氏变换表达式,并得到了此模型主要的排队指标及可靠性指标.     

M/G/1工作休假和休假中止排队

本文分析了一个泊松到达、一般服务的单服务台休假排队，休假策略是工作休假和休假中止．通过嵌入马氏链的方法给出了系统稳态条件，并通过补充变量的方法给出了系统稳态队长的概率母函数。关键词：M／G／1排队系统；工作休假和休假中止；嵌入马氏链；补充变量法     

服务台可修的GI／M（M／PH）／1排队系统

本文首次讨论一个到达间隔为一般分布的可修排队系统。假定服务时间、忙期服务台寿命都服从指疏分布，修复时间是ＰＨ变量。首先证明该系统可转化为一个经典的ＧＩ／￣ＰＨ／１排队模型，然后给出系统在稳态下的各种排队论指标和可靠性指标。     

有Bernoulli休假和可选服务的M/G/1重试反馈排队模型

考虑具有可选服务的M/G/1重试反馈排队模型,其中服务台有Bernoulli休假策略.系统外新到达的顾客服从参数为λ的泊松过程.重试区域只允许队首顾客重试,重试时间服从一般分布.所有的顾客都必须接受必选服务,然而只有其中部分接受可选服务.每个顾客每次被服务完成后可以离开系统或者返回到重试区域.服务台完成一次服务以后,可以休假也可以继续为顾客服务.通过嵌入马尔可夫链法证明了系统稳态的充要条件.利用补充变量的方法得到了稳态时系统和重试区域中队长分布.我们还得到了重试期间服务台处于空闲的概率,重试区域为空的概率以及其他各种指标.并证出在系统中服务员休假和服务台空闲的时间定义为广义休假情况下也具有随机分解特征.     

带有破坏性负顾客的Geo/Geo/1/MWV可修排队系统均衡策略研究

本文研究带有破坏性负顾客的离散时间Geo/Geo/1/MWV可修排队系统的顾客策略行为.当破坏性负顾客到达系统时,会移除正在接受服务的正顾客,同时造成服务台故障.服务台一旦发生损坏,会立刻接受维修,修理时间服从几何分布.服务台在工作休假期间会以较低的服务速率对顾客进行服务.我们求得系统的稳态分布,进一步给出服务台不同状态下的均衡进入率以及系统单位时间的社会收益表达式.最后对均衡进入率和均衡社会收益进行了数值分析.     

部分服务台同步单重休假的M/M/c排队系统

本文研究只允许一部分服务台进入休假状态的M／M／c排队系统，在同步单重休假策略下，给出了稳态指标分布，证明了已知服务台全忙条件下的随机分解结果．     

具有位相型修理的离散时间可修排队系统

本文研究了具有一般独立输入，位相型修理的离散时间可修排队系统，假定服务台对顾客的服务时间和服务台寿命服从几何分布，运用矩阵解析方法我们给出系统嵌入在到达时刻的稳态队长分布和等待时间分布，并证明这些分布均为离散位相型分布．我们也得到在广义服务时间内服务台发生故障次数的分布，证明它服从一个修正的几何分布．我们对离散时间可修排队与连续时间可修排队进行了比较，说明这两种排队系统在一些性能指标方面的区别之处．最后我们通过一些数值例子说明在这类系统中顾客的到达过程、服务时间和服务台的故障率之间的关系．     

Conditional and unconditional distributions forM/G/1 type queues with server vacations

M/G/1 queues with server vacations have been studied extensively over the last two decades. Recent surveys by Boxma [3], Doshi [5] and Teghem [14] provide extensive summary of literature on this subject. More recently, Shanthikumar [11] has generalized some of the results toM/G/1 type queues in which the arrival pattern during the vacations may be different from that during the time the server is actually working. In particular, the queue length at the departure epoch is shown to decompose into two independent random variables, one of which is the queue length at the departure epoch (arrival epoch, steady state) in the correspondingM/G/1 queue without vacations. Such generalizations are important in the analysis of situations involving reneging, balking and finite buffer cyclic server queues. In this paper we consider models similar to the one in Shanthikumar [11] but use the work in the system as the starting point of our investigation. We analyze the busy and idle periods separately and get conditional distributions of work in the system, queue length and, in some cases, waiting time. We then remove the conditioning to get the steady state distributions. Besides deriving the new steady state results and conditional waiting time and queue length distributions, we demonstrate that the results of Boxma and Groenendijk [2] follow as special cases. We also provide an alternative approach to deriving Shanthikumar's [11] results for queue length at departure epochs.     

具有随机N—策略的M／G／1排队系统

本文讨论具有随机N-策略的M/G/1排队系统,采用向量Markov过程方法得到该系统有关的排队指标。上述结果可以看作是普通的和N-策略的M/G/1排队系统的推广。     

服务台可修的M／SM（PH／SM）／1排队系统

本文研究服务台可修的Ｍ／ＳＭ（ＰＨ／ＳＭ）／１排队系统的随机结构和性态．先证明这个可修排队系统在平稳状态下可转化为一个等价的通常排队模型，然后给出服务台的所有稳态可靠性指标及其相关的结果．     

Analysis of the GI/Geo/1 Queue with Disasters

The occurrence of disasters to a queueing system causes all customers to be removed if any are present. Although there has been much research on continuous-time queues with disasters, the discrete-time Geo/Geo/1 queue with disasters has appeared in the literature only recently. We extend this Geo/Geo/1 queue to the GI/Geo/1 queue. We present the probability generating function of the stationary queue length and sojourn time for the GI/Geo/1 queue. In addition, we convert our results into the Geo/Geo/1 queue and the GI/M/1 queue.     

Decompositions of theM/M/1 transition function

Two decompositions are established for the probability transition function of the queue length process in the M/M/1 queue by a simple probabilistic argument. The transition function is expressed in terms of a zero-avoiding probability and a transition probability to zero in two different ways. As a consequence, the M/M/1 transition function can be represented as a positive linear combination of convolutions of the busy-period density. These relations provide insight into the transient behavior and facilitate establishing related results, such as inequalities and asymptotic behavior.     

On the steady-state queue size distribution of the discrete-timeGeo/G/1 queue with repeated customers

In this paper, we study the steady-state queue size distribution of the discrete-timeGeo/G/1 retrial queue. We derive analytic formulas for the probability generating function of the number of customers in the system in steady-state. It is shown that the stochastic decomposition law holds for theGeo/G/1 retrial queue. Recursive formulas for the steady-state probabilities are developed. Computations based on these recursive formulas are numerically stable because the recursions involve only nonnegative terms. Since the regularGeo/G/1 queue is a special case of theGeo/G/1 retrial queue, the recursive formulas can also be used to compute the steady-state queue size distribution of the regularGeo/G/1 queue. Furthermore, it is shown that a continuous-timeM/G/1 retrial queue can be approximated by a discrete-timeGeo/G/1 retrial queue by dividing the time into small intervals of equal length and the approximation approaches the exact when the length of the interval tends to zero. This relationship allows us to apply the recursive formulas derived in this paper to compute the approximate steady-state queue size distribution of the continuous-timeM/G/1 retrial queue and the regularM/G/1 queue.Partially supported by the Natural Sciences and Engineering Research Council of Canada through grant OGP0046415.Partially supported by the Natural Sciences and Engineering Research Council of Canada through grant OGP0105828.     

The effect of different arrival rates on the N-policy of M/G/1 with server setup

It is very important in many real-life systems to decide when the server should start his service because frequent setups inevitably make the operating cost too high. Furthermore, today's systems are too intelligent for the input to be assumed as a simple homogenous Poisson process. In this paper, an M/G/1 queue with general server setup time under a control policy is studied. We consider the case when the arrival rate varies according to the server's status: idle, setup and busy states. We derive the distribution function of the steady-state queue length, as well as the Laplace–Stieltjes transform of waiting time. For this model, the optimal N-value from which the server starts his setup is found by minimizing the total operation cost of the system. We finally investigate the behavior of system operation cost and the optimal N for various arrival rates by a numerical study.     

TheM/G/1 queue with processor sharing and its relation to a feedback queue

The central model of this paper is anM/M/1 queue with a general probabilistic feedback mechanism. When a customer completes his ith service, he departs from the system with probability 1–p(i) and he cycles back with probabilityp(i). The mean service time of each customer is the same for each cycle. We determine the joint distribution of the successive sojourn times of a tagged customer at his loops through the system. Subsequently we let the mean service time at each loop shrink to zero and the feedback probabilities approach one in such a way that the mean total required service time remains constant. The behaviour of the feedback queue then approaches that of anM/G/1 processor sharing queue, different choices of the feedback probabilities leading to different service time distributions in the processor sharing model. This is exploited to analyse the sojourn time distribution in theM/G/1 queue with processor sharing.Some variants are also considered, viz., anM/M/1 feedback queue with additional customers who are always present, and anM/G/1 processor sharing queue with feedback.     

Bayesian prediction inM/M/1 queues

Simple queues with Poisson input and exponential service times are considered to illustrate how well-suited Bayesian methods are used to handle the common inferential aims that appear when dealing with queue problems. The emphasis will mainly be placed on prediction; in particular, we study the predictive distribution of usual measures of effectiveness in anM/M/1 queue system, such as the number of customers in the queue and in the system, the waiting time in the queue and in the system, the length of an idle period and the length of a busy period.     

A note on the C2/G/1 queue and the C2/G/1 loss system

In this note, we consider the steady-state probability of delay (PW) in the C₂/G/1 queue and the steady-state probability of loss (p_loss) in the C₂/G/1 loss system, in both of which the interarrival time has a two-phase Coxian distribution. We show that, for c_X ²<1, where c_X is the coefficient of variation of the interarrival time, both p_loss and PW are increasing in β(s), the Laplace–Stieltjes transform of the general service-time distribution. This generalises earlier results for the GE₂/G/1 queue and the GE₂/G/1 loss system. The practical significance of this is that, for c_X ²<1, p_loss in the C₂/G/1 loss system and PW in the C₂/G/1 queue are both increasing in the variability of the service time. This revised version was published online in June 2006 with corrections to the Cover Date.