scholarly journals Analysis of an MMAP/PH1, PH2/N/∞ queueing system operating in a random environment

2014 ◽  
Vol 24 (3) ◽  
pp. 485-501 ◽  
Author(s):  
Chesoong Kim ◽  
Alexander Dudin ◽  
Sergey Dudin ◽  
Olga Dudina
Keyword(s):  
Random Environment ◽  
Queueing System ◽  
Arrival Process ◽  
Service Time Distribution ◽  
Contact Center ◽  
Phase Type Distribution ◽  
Phase Type ◽  
Multi Server

Abstract A multi-server queueing system with two types of customers and an infinite buffer operating in a random environment as a model of a contact center is investigated. The arrival flow of customers is described by a marked Markovian arrival process. Type 1 customers have a non-preemptive priority over type 2 customers and can leave the buffer due to a lack of service. The service times of different type customers have a phase-type distribution with different parameters. To facilitate the investigation of the system we use a generalized phase-type service time distribution. The criterion of ergodicity for a multi-dimensional Markov chain describing the behavior of the system and the algorithm for computation of its steady-state distribution are outlined. Some key performance measures are calculated. The Laplace-Stieltjes transforms of the sojourn and waiting time distributions of priority and non-priority customers are derived. A numerical example illustrating the importance of taking into account the correlation in the arrival process is presented

scholarly journals A discrete single server queue with Markovian arrivals and phase type group services

1995 ◽  
Vol 8 (2) ◽  
pp. 151-176 ◽  
Author(s):  
Attahiru Sule Alfa ◽  
K. Laurie Dolhun ◽  
S. Chakravarthy
Keyword(s):  
Steady State ◽  
Queueing System ◽  
State Probability ◽  
Arrival Process ◽  
Single Server ◽  
Probability Vector ◽  
Phase Type Distribution ◽  
Steady State Probability ◽  
Phase Type ◽  
Number Of Customers

We consider a single-server discrete queueing system in which arrivals occur according to a Markovian arrival process. Service is provided in groups of size no more than M customers. The service times are assumed to follow a discrete phase type distribution, whose representation may depend on the group size. Under a probabilistic service rule, which depends on the number of customers waiting in the queue, this system is studied as a Markov process. This type of queueing system is encountered in the operations of an automatic storage retrieval system. The steady-state probability vector is shown to be of (modified) matrix-geometric type. Efficient algorithmic procedures for the computation of the rate matrix, steady-state probability vector, and some important system performance measures are developed. The steady-state waiting time distribution is derived explicitly. Some numerical examples are presented.

A BMAP/PH/N SYSTEM WITH IMPATIENT REPEATED CALLS

2007 ◽  
Vol 24 (03) ◽  
pp. 293-312 ◽  
Author(s):  
VALENTINA I. KLIMENOK ◽  
DMITRY S. ORLOVSKY ◽  
ALEXANDER N. DUDIN
Keyword(s):  
Markov Chain ◽  
Time Distribution ◽  
Arrival Process ◽  
Service Time Distribution ◽  
State Distribution ◽  
Unsuccessful Attempt ◽  
Batch Markovian Arrival Process ◽  
Repeated Calls ◽  
Phase Type ◽  
Multi Server

A multi-server queueing model with a Batch Markovian Arrival Process, phase-type service time distribution and impatient repeated customers is analyzed. After any unsuccessful attempt, the repeated customer leaves the system with the fixed probability. The behavior of the system is described in terms of continuous time multi-dimensional Markov chain. Stability condition and an algorithm for calculating the stationary state distribution of this Markov chain are obtained. Main performance measures of the system are calculated. Numerical results are presented.

scholarly journals Queueing System with Heterogeneous Customers as a Model of a Call Center with a Call-Back for Lost Customers

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Sergey Dudin ◽  
Chesoong Kim ◽  
Olga Dudina ◽  
Janghyun Baek
Keyword(s):  
Call Center ◽  
Queueing System ◽  
Optimal Choice ◽  
Arrival Process ◽  
Finite Buffers ◽  
Different Types ◽  
Sojourn Time Distribution ◽  
Multiserver Queueing System

A multiserver queueing system with infinite and finite buffers, two types of customers, and two types of servers as a model of a call center with a call-back for lost customers is investigated. Type 1 customers arrive to the system according to a Markovian arrival process. All rejected type 1 customers become type 2 customers. Typer,r=1,2, servers serve typercustomers if there are any in the system and serve typer′,r′=1,2,  r′≠r,customers if there are no typercustomers in the system. The service times of different types of customers have an exponential distribution with different parameters. The steady-state distribution of the system is analyzed. Some key performance measures are calculated. The Laplace-Stieltjes transform of the sojourn time distribution of type 2 customers is derived. The problem of optimal choice of the number of each type servers is solved numerically.

scholarly journals Priority Multi-Server Queueing System with Heterogeneous Customers

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1501
Author(s):  
Valentina Klimenok ◽  
Alexander Dudin ◽  
Vladimir Vishnevsky
Keyword(s):  
Information Transmission ◽  
Performance Measures ◽  
Queueing System ◽  
Real Life ◽  
Telecommunication Networks ◽  
Arrival Process ◽  
Phase Type Distribution ◽  
Arrival Times ◽  
Process Measurements ◽  
Multi Server

In this paper, we analyze a multi-server queueing system with heterogeneous customers that arrive according to a marked Markovian arrival process. Customers of two types differ in priorities and parameters of phase type distribution of their service time. The queue under consideration can be used to model the processes of information transmission in telecommunication networks in which often the flow of information is the superposition of several types of flows with correlation of inter-arrival times within each flow and cross-correlation. We define the process of information transmission as the multi-dimensional Markov chain, derive the generator of this chain and compute its stationary distribution. Expressions for computation of various performance measures of the system, including the probabilities of loss of customers of different types, are presented. Output flow from the system is characterized. The presented numerical results confirm the high importance of account of correlation in the arrival process. The values of important performance measures for the systems with the correlated arrival process are essentially different from the corresponding values for the systems with the stationary Poisson arrival process. Measurements in many real world systems show poor approximation of real flows by such an arrival process. However, this process is still popular among the telecommunication engineers due to the evident existing gap between the needs of adequately modeling the real life systems and the current state of the theory of algorithmic methods of queueing theory.

scholarly journals A Queueing System with Heterogeneous Impatient Customers and Consumable Additional Items

2017 ◽  
Vol 27 (2) ◽  
pp. 367-384 ◽  
Author(s):  
Janghyun Baek ◽  
Olga Dudina ◽  
Chesoong Kim
Keyword(s):  
Waiting Time ◽  
Queueing System ◽  
Optimization Problems ◽  
Markovian Arrival Process ◽  
Arrival Process ◽  
Single Server ◽  
Finite Buffer ◽  
Threshold Strategy

Abstract A single-server queueing system with a marked Markovian arrival process of heterogeneous customers is considered. Type-1 customers have limited preemptive priority over type-2 customers. There is an infinite buffer for type-2 customers and no buffer for type-1 customers. There is also a finite buffer (stock) for consumable additional items (semi-products, half-stocks, etc.) which arrive according to the Markovian arrival process. Service of a customer requires a fixed number of consumable additional items depending on the type of the customer. The service time has a phase-type distribution depending on the type of the customer. Customers in the buffer are impatient and may leave the system without service after an exponentially distributed amount of waiting time. Aiming to minimize the loss probability of type-1 customers and maximize throughput of the system, a threshold strategy of admission to service of type-2 customers is offered. Service of type-2 customer can start only if the server is idle and the number of consumable additional items in the stock exceeds the fixed threshold. Stationary distributions of the system states and the waiting time are computed. In the numerical example, we show some interesting effects and illustrate a possibility of application of the presented results for solution of optimization problems.

scholarly journals The Queueing Model MAP|PH|1|N with Feedback Operating in a Markovian Random Environment

2016 ◽  
Vol 34 (2) ◽  
Author(s):  
A.N. Dudin ◽  
A.V. Kazimirsky ◽  
V.I. Klimenok ◽  
L. Breuer ◽  
U. Krieger
Keyword(s):  
Random Environment ◽  
Queueing Systems ◽  
Arrival Process ◽  
Single Server ◽  
Service Time Distribution ◽  
Finite Buffer ◽  
Phase Type ◽  
Server Queue ◽  
Finite State ◽  
Markovian Random Environment

Queueing systems with feedback are well suited for the description of message transmission and manufacturing processes where a repeated service is required. In the present paper we investigate a rather general single server queue with a Markovian Arrival Process (MAP), Phase-type (PH) service-time distribution, a finite buffer and feedback which operates in a random environment. A finite state Markovian random environment affects the parameters of the input and service processes and the feedback probability. The stationary distribution of the queue and of the sojourn times as well as the loss probability are calculated. Moreover, Little’s law is derived.

scholarly journals Analysis of Multiserver Queueing System with Opportunistic Occupation and Reservation of Servers

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Bin Sun ◽  
Moon Ho Lee ◽  
Sergey A. Dudin ◽  
Alexander N. Dudin
Keyword(s):  
System Performance ◽  
Queueing System ◽  
Optimal Choice ◽  
Preemptive Priority ◽  
Rate Dependent ◽  
Service Type ◽  
The Subject ◽  
Multiserver Queueing System

We consider a multiserver queueing system with two input flows. Type-1 customers have preemptive priority and are lost during arrival only if all servers are occupied by type-1 customers. If all servers are occupied, but some provide service to type-2 customers, service of type-2 customer is terminated and type-1 customer occupies the server. If the number of busy servers is less than the thresholdMduring type-2 customer arrival epoch, this customer is accepted. Otherwise, it is lost or becomes a retrial customer. It will retry to obtain service. Type-2 customer whose service is terminated is lost or moves to the pool of retrial customers. The service time is exponentially distributed with the rate dependent on the customer’s type. Such queueing system is suitable for modeling cognitive radio. Type-1 customers are interpreted as requests generated by primary users. Type-2 customers are generated by secondary or cognitive users. The problem of optimal choice of the thresholdMis the subject of this paper. Behavior of the system is described by the multidimensional Markov chain. Its generator, ergodicity condition, and stationary distribution are given. The system performance measures are obtained. The numerical results show the effectiveness of considered admission control.

scholarly journals Modified approach to the characterization of adrenal nodules using a standard abdominal magnetic resonance imaging protocol

2017 ◽  
Vol 50 (1) ◽  
pp. 19-25 ◽  
Author(s):  
António P. Matos ◽  
Richard C. Semelka ◽  
Vasco Herédia ◽  
Mamdoh AlObaidiy ◽  
Filipe Veloso Gomes ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Imaging Protocol ◽  
Intensity Index ◽  
Abdominal Magnetic Resonance Imaging ◽  
Phase Type ◽  
The Mean ◽  
Magnetic Resonance Imaging Protocol ◽  
Type 3

Abstract Objective: To describe a modified approach to the evaluation of adrenal nodules using a standard abdominal magnetic resonance imaging protocol. Materials and Methods: Our sample comprised 149 subjects (collectively presenting with 132 adenomas and 40 nonadenomas). The adrenal signal intensity index was calculated. Lesions were grouped by pattern of enhancement (PE), according to the phase during which the wash-in peaked: arterial phase (type 1 PE); portal venous phase (type 2 PE); and interstitial phase (type 3 PE). The relative and absolute wash-out values were calculated. To test for mean differences between adenomas and nonadenomas, Student's t-tests were used. Receiver operating characteristic curve analysis was also performed. Results: The mean adrenal signal intensity index was significantly higher for the adenomas than for the nonadenomas (p < 0.0001). Chemical shift imaging showed a sensitivity and specificity of 94.4% and 100%, respectively, for differentiating adenomas from nonadenomas. Of the adenomas, 47.6%, 48.5%, and 3.9%, respectively, exhibited type 1, 2, and 3 PEs. For the mean wash-in proportions, significant differences were found among the enhancement patterns. The wash-out calculations revealed a trend toward better lesion differentiation for lesions exhibiting a type 1 PE, showing a sensitivity and specificity of 71.4% and 80.0%, respectively, when the absolute values were referenced, as well as for lesions exhibiting a type 2 PE, showing a sensitivity and specificity of 68.0% and 100%, respectively, when the relative values were referenced. The calculated probability of a lipid-poor lesion that exhibited a type 3 PE being a nonadenoma was > 99%. Conclusion: Subgrouping dynamic enhancement patterns yields high diagnostic accuracy in differentiating adenomas from nonadenomas.

Characterization for the queueing system M/G/∞

1973 ◽  
Vol 74 (1) ◽  
pp. 141-143 ◽  
Author(s):  
D. N. Shanbhag
Keyword(s):  
Distribution Function ◽  
Time Distribution ◽  
Queueing System ◽  
Arrival Process ◽  
Service Time Distribution ◽  
Traffic Intensity ◽  
Infinitely Divisible ◽  
Waiting Room ◽  
Room Size ◽  
Arrival Intensity

Consider a queueing system M/G/s with the arrival intensity λ, the service time distribution function B(t) (B(0) < 1) having a finite mean and the waiting room size N ≤ ∞. If s < ∞ and N = ∞, we shall also assume that its relative traffic intensity is less than 1. Since the arrival process of this system is Poisson, it is immediate that in this case the distribution of the number of arrivals during an interval is infinitely divisible.

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