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2010, European Journal of Operational Research
We study M=M=c queues (c ¼ 1, 1 < c < 1 and c ¼ 1Þ in a 2-phase (fast and slow) Markovian random environment, with impatient customers. The system resides in the fast phase (phase 1) an exponentially distributed random time with parameter g and the arrival and service rates are k and l, respectively. The corresponding parameters for the slow phase (phase 0) are c, k 0 , and l 0 ð6 lÞ. When in the slow phase, customers become impatient. That is, each customer, upon arrival, activates an individual timer, exponentially distributed with parameter n. If the system does not change its environment from 0 to 1 before the customer's timer expires, the customer abandons the queue never to return.
Stochastic Processes and Models in Operations Research, 2000
A single-server queuing system with impatient customers and Coxian service is examined. It is assumed that arrivals are Poisson with a constant rate. When the server is busy upon an arrival, customer joins the queue and there is an infinite capacity of the queue. Since the variance of the service time is relatively high, the service time distribution is characterized by k-phase Cox distribution. Due to the high variability of service times and since some of the services take extremely long time, customers not only in the queue, but also in the service may become impatient. Each customer, upon arrival, activates an individual timer and starts his patience time. The patience time for each customer is a random variable which has exponential distribution. If the service does not completed before the customer's time expires, the customer abandons the queue never to return. The model is expressed as birth-and-death process and the balance equations are provided.
OPSEARCH, 2019
This paper investigates an optimal K-policy for a two-server Markovian queueing system M∕(M 1 , M 2)∕2∕(B 1 , B 2), with one fast server S 1 and one slow server S 2 , using the matrix analytic method. Two buffers B 1 and B 2 are organized to form waiting lines of customers in which, buffer B 1 is of finite size K(< ∞) and buffer B 2 is of infinite capacity. Buffer B 1 stalls customers who arrive when the system size (queue + service) is less than (K + 1) and dispatches a customer to the fast server S 1 only after S 1 completes its previous service. This K-policy is of threshold type which deals with controlling of informed customers and hence the customers have better choice of choosing the fast server routing through the buffer B 1. The (K + 2)-nd customer who arrives when the number of customers present in the system is exactly (K + 1) has the Hobson's choice of getting service from the slow server S 2. Buffer B 2 accommodates other customers who arrive when the number of customers present in the system is (K + 2) or more and feeds them one after another to either buffer B 1 or the sever S 2 whichever event can first accept the customer at the head-of-the-line in B 2. Queue length processes of interest are (1) q 1 = lim t→∞ X 1 (t) and (2) q 2 = lim t→∞ X 2 (t) , where X 1 (t) represents the number of customers who are in the buffers B 1 and B 2 and also in the service with server S 1 at time 't' and X 2 (t) represents the number of customers available with server S 2 only. The bi-variate random sequence (t) = (X 1 (t), X 2 (t)) of the system size (queue + service) forms a quasi-birth and death process (QBD). Steady state characteristics, and some of the performance measures such as the expected queue length, the probability that each server is busy etc are obtained. Numerical illustrations are provided based on the average cost function to explore the methodology of finding the best K-policy which minimizes the mean sojourn time of customers. Keywords QBD processes and M∕(M 1 ,M 2)∕2∕(B 1 ,B 2) • Fast server • Slow server • Matrix analytic method • Stationary distribution
We consider the joint dynamics of the instantaneous and exponentially averaged queue length in an M/M/1/K queue. A system of ordinary dierential equations is derived for the joint stationary distribution of the instantaneous and the exponen- tially averaged queue length. The solution of the system of equations is obtained in a few special cases. Three dierent numerical approaches are presented to find the stationary distribution in the general case. Some results obtained with the numerical methods are presented and the eciency of the numerical approaches is discussed. In addition, we describe how the model can be extended to a more complex situa- tion which contains a rejection mechanism that randomly drops incoming customers with a dropping probability that depends on the current state of the averaged queue length.
Queueing Systems, 2011
Consider a number of parallel queues, each with an arbitrary capacity and multiple identical exponential servers. The service discipline in each queue is firstcome-first-served (FCFS). Customers arrive according to a state-dependent Poisson process. Upon arrival, a customer joins a queue according to a state-dependent policy or leaves the system immediately if it is full. No jockeying among queues is allowed. An incoming customer to a parallel queue has a general patience time dependent on that queue after which he/she must depart from the system immediately. Parallel queues are of two types: type 1, wherein the impatience mechanism acts on the waiting time; or type 2, a single server queue wherein the impatience acts on the sojourn time. We prove a key result, namely, that the state process of the system in the long run converges in distribution to a well-defined Markov process. Closed-form solutions for the probability density function of the virtual waiting time of a queue of type 1 or the offered sojourn time of a queue of type 2 in a given state are derived which are, interestingly, found to depend only on the local state of the queue. The efficacy of the approach is illustrated by some numerical examples.
International Journal of Science and Research (IJSR)
The ultimate objective of the analysis of queuing systems is to understand the behaviour of their underlying process so that informed and intelligent decisions can be made by the management. The application of queuing concepts is an attempt to minimize cost through minimization of inefficiency and delays in a system. Various methods of solving queuing problems have been proposed. In this study we have explored single –server Markovian queuing model with both interarrival and service times following exponential distribution with parameters and , respectively, and unlimited queue size with FIFO queuing discipline and unlimited customer population. We apply this model to catering data and estimate parameters for the same. A sensitivity analysis is the carried out to evaluate stability of the system.
Journal of Applied Probability, 1981
The paper studies a queuing model with Poisson arrival process and bulk service. The server serves the customers in batches of fixed size b, and the service time is assumed to be exponentially distribution. The model is analyzed to find the steady-state distribution of the number of customers stranded following each service. The approach adopted is based on discrete-time Markov chains, instead of Laplace transforms that is usually used in literature. A simulation study is carried out to estimate the expected number of stranded customers at any point of time, its variance and the downside risk for given values of the system parameters.
2011
Many-Server Queues with Time-Varying Arrivals, Customer A bandonment and Non-Exponential Distributions Yunan Liu This thesis develops deterministic heavy-traffic fluid appr oximations for many-server stochastic queueing models. The queueing models, with many homogenous servers working independently in parallel, are intended to model largescale service systems such as call centers and health care systems. Such models also have been e mploy d to study communication, computing and manufacturing systems. The heavytraffic approximations yield relatively simple formulas for quantities describing syst em performance, such as the expected number of customers waiting in the queue. The new performance approximations are valuable because, i n the generality considered, these complex systems are not amenable to exact mathem atical analysis. Since the approximate performance measures can be computed quite rap idly, they usefully complement more cumbersome computer simulation. Thus these heavy -t...
International Journal of Mathematics and Mathematical Sciences, 1992
This paper introduces a bulk queucing system with a single server processing groups of customers of a variable size. If upon completion of service the queueing level is at least r the server takes a batch of size r and processes it a random time arbitrarily distributed. If the qucueing level is less than r the server idles until the queue accumulates r customers in total.
Queueing Systems, 2008
We consider an infinite-server queue, where the arrival and service rates are both governed by a semi-Markov process that is independent of all other aspects of the queue. In particular, we derive a system of equations that are satisfied by various "parts" of the generating function of the steady-state queue-length, while assuming that all arrivals bring an amount of work to the system that is either Erlang or hyperexponentially distributed. These equations are then used to show how to derive all moments of the steady-state queue-length. We then conclude by showing how these results can be slightly extended, and used, along with a transient version of Little's law, to generate rigorous approximations of the steady-state queue-length in the case that the amount of work brought by a given arrival is of an arbitrary distribution.
Journal of Industrial and Management Optimization, 2021
We study the \begin{document}$ MAP/M/s+G $\end{document} queueing model that arises in various multi-server engineering problems including telephone call centers, under the assumption of MAP (Markovian Arrival Process) arrivals, exponentially distributed service times, infinite waiting room, and generally distributed patience times. Using sample-path arguments, we propose to obtain the steady-state distribution of the virtual waiting time and subsequently the other relevant performance metrics of interest via the steady-state solution of a certain Continuous Feedback Fluid Queue (CFFQ). The proposed method is exact when the patience time is a discrete random variable and is asymptotically exact when it is continuous/hybrid, for which case discretization of the patience time distribution is required giving rise to a computational complexity depending linearly on the number of discretization levels. Additionally, a novel method is proposed to accurately obtain the first passage time d...
The paper studies the queuing model with three non-identical exponential servers S 1 , S 2 and S 3 and provides a matrix-geometric solution for an underlying quasi birth-and-death (QBD) queue of an M/M(S 1),M(S 2 ,S 3)/3/(m,∞) system. Customers arrive individually according to a Poisson process and form two parallel queues, say q 1 and q 2. The size of q 1 represents the system length (queue+server) of a finite queueing facility M/M(S 1)/1/(m+1) and the size of the q 2 accounts the system length (queue+server) of a two-server queue M/M(S 2 ,S 3)/2 facility. Queue management for each of q 1 and q 2 is through a 'First Come First Served (FCFS)' basis but according to the norms of an m-policy. At an arrival instant, if the size of q 1 is strictly less than 'm', the new arrival is assigned to q 1 with an unknown probability P 1 (=1-Pr(q 1 =m)); otherwise it is assigned to q 2 with probability (1-P 1) subject to a condition that switching from q 1 to q 2 and vice versa is to be avoided. At every service completion epoch, the dispatching mechanism of the m-policy either assigns a customer of q 1 > 0 to server S 1 or a customer of q 2 >0 to server S 2 , if available, or otherwise to S 3. The underlying QBD process representing the number of customers in the system under study is formulated as a bi-variate queue length sequence X=(q 1 = i, q 2 = j) defined on the two-dimensional state space Ω ={(i, j): 0≤ i ≤ m, j ≥ 0}. Explicit expressions for the stationary condition, stationary distribution of X, marginal expected values of q 1 and q 2 , and the probability P 1 are obtained. The paper also constructs a formal linear programming to find an optimal value of m, corresponding to the minimum cost.
Science Journal of Applied Mathematics and Statistics, 2015
In this study a two stage queueing model is analyzed. At first stage there is a single server having exponential service time with parameter and no waiting is allowed in front of this server. There are two parallel phase-type servers at second stage and these parallel servers have exponential service time with parameter. Arrivals to this system is Poisson with parameter. An arriving customer to this system has service if the server at first stage is available or leaves the system if the server is busy where the first loss occurs. After having service in first stage the customer proceeds to the second stage, if both of the phase-type parallel servers in second stage are available the customer chooses one of these servers with probability 0.50 or leaves the system if any of these servers in second stage is busy so the second loss occurs. A customer who has service at both stages leaves the system. The number of customers in this model is represented by a 3-diamensional Markov chain and Kolmogorov differential equations are obtained. After that mean number of customers and mean waiting time in the system is obtained by limit probabilities. We have shown that the customer numbers at first and second stages are dependent to each other. The numerical analysis of obtained performance measures are shown by a numeric example. Finally the graphs of loss probabilities and measure of performances given for some values of arrival rate and the service parameters.
Applied Mathematical Modelling, 1996
In this paper we study the time-dependent analysis of a limited capacity queueing model with the bulk arrival rate depending upon the nature of service available in the system. The customers arrive in the system in batches of size x, which is a random variable, and the service consists of two stages, one is essential (first stage) while the other may be inessential. The decision to offer the inessential service depends upon the size of the system. However, if this inessential service is temporarily suspended, the arrival rate of the customers decreases. Laplace transforms (in time) of the different probability generating functions describing the system size under various conditions of service and the expected system size are derived. Steady-state results consequently follow.
Thailand Statistician, 2017
The paper investigates a M / M ( b , b ) /1 queuing model with bulk service. The server serves the customers in batches of fixed size b , and the service time is assumed to be exponentially distribution. Customers arrive to the system as a Poisson process and may renege after waiting in the queue for an exponentially distributed time. The reneging of a customer depends on the state of the system. The model is analyzed to find the different measures of effectiveness of the model. The approach adopted is based on embedded Markov chains.
Operations Research, 2004
For a broad class of discrete-and continuous-time queueing systems, we show that the stationary number of customers in system (queue plus servers) is the sum of two independent random variables, one of which is the stationary number of customers in queue and the other is the number of customers that arrive during the time a customer spends in service. We call this relation an invariance relation in the sense that it does not change for a variety of single-sever queues (with batch arrivals and batch services) and some of multi-server queues (with batch arrivals and deterministic service times) that satisfy a certain set of assumptions. Making use of this relation, we also present a simple method of deriving the stationary distributions of the numbers in queue and in system as well as some of their properties. This is illustrated by several examples, which show that new simple derivations of old results as well as new results can be obtained in a unified manner. Furthermore, we show that the invariance relation and the method we are presenting are easily generalized to analyze queues with BMAP (Batch Markovian Arrival Process) arrivals. Most of the results are presented under the discrete-time setting. The corresponding continuous-time results, however, are covered as well because deriving the results for continuous-time queues runs exactly parallel to that for their discrete-time counterparts.
Journal of Probability and Statistics
This paper presents analytically explicit results for the distribution of the number of customers served during a busy period for special cases of the M/G/1 queues when initiated with m customers. The functional equation for the Laplace transform of the number of customers served during a busy period is widely known, but several researchers state that, in general, it is not easy to invert it except for some simple cases such as M/M/1 and M/D/1 queues. Using the Lagrange inversion theorem, we give an elegant solution to this equation. We obtain the distribution of the number of customers served during a busy period for various service-time distributions such as exponential, deterministic, Erlang-k, gamma, chi-square, inverse Gaussian, generalized Erlang, matrix exponential, hyperexponential, uniform, Coxian, phase-type, Markov-modulated Poisson process, and interrupted Poisson process. Further, we also provide computational results using our method. The derivations are very fast and ...
IEEE Journal on Selected Areas in Communications, 1991
This paper is concerned with an M / G / I FCFS queue with twd types of customers, viz. (1) ordinary customers who arrive according to a Poisson process, and (2) permanent customers, who immediately return to the end of the queue after having received a service. The influence of the permanent customers on queue length and sojourn times of the Poisson customers is studied, using results from queueing theory and from the theory of branching processes. In particular it is shown, for the case that the service time distributions of the Poisson customers and all K permanent customers are negative exponential with identical means, that the queue length and sojourn time distributions of the Poisson customers are the ( K + 1 )-fold convolution of those for the case without permanent customers.
Probability in the Engineering and Informational Sciences, 1992
We study a generalization of the GI/G/l queue in which the server is turned off at the end of each busy period and is reactivated only when the sum of the service times of all waiting customers exceeds a given threshold of size D. Using the concept of a “randomly selected” arriving customer, we obtain as our main result a relation that expresses the waiting-time distribution of customers in this model in terms of characteristics associated with a corresponding standard GI/G/1 queue, obtained by setting D = 0. If either the arrival process is Poisson or the service times are exponentially distributed, then this representation of the waiting-time distribution can be specialized to yield explicit, transform-free formulas; we also derive, in both of these cases, the expected customer waiting times. Our results are potentially useful, for example, for studying optimization models in which the threshold D can be controlled.
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