scholarly journals The properties of the queuing model with the parallel structure

2020 ◽  
pp. 79-82
Author(s):  
O. A. Chechelnitsky
Keyword(s):  
Stationary Regime ◽  
Parallel Structure ◽  
Stochastic Dynamic ◽  
General Function ◽  
Expected Number ◽  
Queuing Model ◽  
Poisson Input ◽  
Infinite Server Queues ◽  
Result Analysis ◽  
Number Of Customers

The present article is devoted to research the multi-channelk model with the parallel structure. It means that we consider the model which consists of two infinite-server queues. The service time in the each system has general function of distribution. In this case the stochastic dynamic of our model cannot be defined by Markov chain. As a result, analysis of such models is much more difficult than that of the corresponding Markovian queueing models. Besides we assume that customers arrive to our model according a bivariate Poisson input flow. This input process is characterized by the fact that customers arrive according to a bivariate Poisson flow simultaneously. We consider the number of customers in the systems at time t. This stochastic process describes the state of our model. In present paper we find the limit joint distribution of the number of customers in the systems. In a general way (by differentiating the corresponding generating function.) we obtain the main characteristics of this distribution, such as the expected number of customers in the nodes, its variance and correlation. In the case when parameters of our model dependent on the parameter n (number of series) the limit normal distribution was obtained for the service process in the stationary regime.

scholarly journals Applications of Queuing Theory in Quantitative Business Analysis

2020 ◽  
pp. 253-257
Keyword(s):  
Quantitative Analysis ◽  
Waiting Time ◽  
Queuing Theory ◽  
Single Channel ◽  
Analysis Approach ◽  
Single Server ◽  
Expected Number ◽  
Queuing Model ◽  
Infinite Population ◽  
Expected Waiting Time

Queuing Theory provides the system of applications in many sectors in life cycle. Queuing Structure and basic components determination is computed in queuing model simulation process. Distributions in Queuing Model can be extracted in quantitative analysis approach. Differences in Queuing Model Queue discipline, Single and Multiple service station with finite and infinite population is described in Quantitative analysis process. Basic expansions of probability density function, Expected waiting time in queue, Expected length of Queue, Expected size of system, probability of server being busy, and probability of system being empty conditions can be evaluated in this quantitative analysis approach. Probability of waiting ‘t’ minutes or more in queue and Expected number of customer served per busy period, Expected waiting time in System are also computed during the Analysis method. Single channel model with infinite population is used as most common case of queuing problems which involves the single channel or single server waiting line. Single Server model with finite population in test statistics provides the Relationships used in various applications like Expected time a customer spends in the system, Expected waiting time of a customer in the queue, Probability that there are n customers in the system objective case, Expected number of customers in the system

Transient and steady-state analysis of a queuing system having customers’ impatience with threshold

2019 ◽  
Vol 53 (5) ◽  
pp. 1861-1876 ◽  
Author(s):  
Sapana Sharma ◽  
Rakesh Kumar ◽  
Sherif Ibrahim Ammar
Keyword(s):  
Steady State ◽  
Probability Generating Function ◽  
Threshold Value ◽  
Steady State Solution ◽  
Function Technique ◽  
Single Server ◽  
Queuing Model ◽  
Queuing Models ◽  
Special Cases ◽  
Number Of Customers

In many practical queuing situations reneging and balking can only occur if the number of customers in the system is greater than a certain threshold value. Therefore, in this paper we study a single server Markovian queuing model having customers’ impatience (balking and reneging) with threshold, and retention of reneging customers. The transient analysis of the model is performed by using probability generating function technique. The expressions for the mean and variance of the number of customers in the system are obtained and a numerical example is also provided. Further the steady-state solution of the model is obtained. Finally, some important queuing models are derived as the special cases of this model.

Development of a Heuristics Model for Simplifying a Multi-Channel Queuing System

2011 ◽  
Vol 367 ◽  
pp. 647-652
Author(s):  
B. Kareem ◽  
A. A. Aderoba
Keyword(s):  
Waiting Time ◽  
Channel Model ◽  
Single Channel ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Queuing Models ◽  
Significant Difference ◽  
Number Of Customers ◽  
Proportionality Principle

Queuing model has been discussed widely in literature. The structures of queuing systems are broadly divided into three namely; single, multi-channel, and mixed. Equations for solving these queuing problems vary in complexity. The most complex of them is the multi-channel queuing problem. A heuristically simplified equation based on relative comparison, using proportionality principle, of the measured effectiveness from the single and multi-channel models seems promising in solving this complex problem. In this study, six different queuing models were used from which five of them are single-channel systems while the balance is multi-channel. Equations for solving these models were identified based on their properties. Queuing models’ performance parameters were measured using relative proportionality principle from which complexity of multi-channel system was transformed to a simple linear relation of the form = . This showed that the performance obtained from single channel model has a linear relationship with corresponding to multi-channel, and is a factor which varies with the structure of queuing system. The model was tested with practical data collected on the arrival and departure of customers from a cocoa processing factory. The performances obtained based on average number of customers on line , average number of customers in the system , average waiting time in line and average waiting time in the system, under certain conditions showed no significant difference between using heuristics and analytical models.

Busy period in GIX/G/∞

1996 ◽  
Vol 33 (3) ◽  
pp. 815-829 ◽  
Author(s):  
Liming Liu ◽  
Ding-Hua Shi
Keyword(s):  
Service Time ◽  
Busy Period ◽  
Random Variables ◽  
Batch Service ◽  
Idle Period ◽  
Special Cases ◽  
Infinite Server Queues ◽  
General Relations ◽  
Number Of Customers ◽  
Busy Cycle

Busy period problems in infinite server queues are studied systematically, starting from the batch service time. General relations are given for the lengths of the busy cycle, busy period and idle period, and for the number of customers served in a busy period. These relations show that the idle period is the most difficult while the busy cycle is the simplest of the four random variables. Renewal arguments are used to derive explicit results for both general and special cases.

scholarly journals Markovian Queueing System with Discouraged Arrivals and Self-Regulatory Servers

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
K. V. Abdul Rasheed ◽  
M. Manoharan
Keyword(s):  
Queueing System ◽  
Queueing Systems ◽  
Single Server ◽  
Expected Number ◽  
Multiple Servers ◽  
Special Cases ◽  
Service Rates ◽  
Expected Waiting Time ◽  
Number Of Customers ◽  
Steady State Probabilities

We consider discouraged arrival of Markovian queueing systems whose service speed is regulated according to the number of customers in the system. We will reduce the congestion in two ways. First we attempt to reduce the congestion by discouraging the arrivals of customers from joining the queue. Secondly we reduce the congestion by introducing the concept of service switches. First we consider a model in which multiple servers have three service ratesμ1,μ2, andμ(μ1≤μ2<μ), say, slow, medium, and fast rates, respectively. If the number of customers in the system exceeds a particular pointK1orK2, the server switches to the medium or fast rate, respectively. For this adaptive queueing system the steady state probabilities are derived and some performance measures such as expected number in the system/queue and expected waiting time in the system/queue are obtained. Multiple server discouraged arrival model having one service switch and single server discouraged arrival model having one and two service switches are obtained as special cases. A Matlab program of the model is presented and numerical illustrations are given.

Confidence Intervals for Performance Measures of M/M/1 Queue with Constant Retrial Policy

2015 ◽  
Vol 32 (06) ◽  
pp. 1550046
Author(s):  
Dmitry Efrosinin ◽  
Anastasia Winkler ◽  
Pinzger Martin
Keyword(s):  
Performance Measures ◽  
Queueing System ◽  
Retrial Queue ◽  
Threshold Level ◽  
Stationary Regime ◽  
Parametric Estimation ◽  
Threshold Policy ◽  
System Parameters ◽  
Number Of Customers ◽  
The Given

We consider the problem of estimation and confidence interval construction of a Markovian controllable queueing system with unreliable server and constant retrial policy. For the fully observable system the standard parametric estimation technique is used. The arrived customer finding a free server either gets service immediately or joins a retrial queue. The customer at the head of the retrial queue is allowed to retry for service. When the server is busy, it is subject to breakdowns. In a failed state the server can be repaired with respect to the threshold policy: the repair starts when the number of customers in the system reaches a fixed threshold level. To obtain the estimates for the system parameters, performance measures and optimal threshold level we analyze the system in a stationary regime. The performance measures including average cost function for the given cost structure are presented in a closed matrix form.

scholarly journals Effective school cooperative-mart queuing system

2017 ◽  
Vol 13 (4-1) ◽  
pp. 412-415
Author(s):  
Ahmad Ridhuan Hamdan ◽  
Ruzana Ishak ◽  
Mohd Fais Usop
Keyword(s):  
Queuing Theory ◽  
Service Level ◽  
Queuing System ◽  
Queuing Model ◽  
Effective School ◽  
Service Centre ◽  
Large Numbers ◽  
Female Data ◽  
Waiting Line ◽  
Number Of Customers

Queuing Theory is a branch of knowledge in operation research that concerning the analysis of queues when a customer arrives at a service centre and shall queue in a line to get some service. The theory pays attention to how organizations can serve a large number of customers who demand a quality services and a queue of customers waiting to be served. Eventually, the store owners have to attend to large numbers of customers at a time have attempted to measure and manage queues to reduce the customer procession time. Besides, to increase sales and profit, productivity and operation efficiency, satisfaction levels and customer loyalty in using the service provided. In line to the situation, this study is to determine the effectiveness of the waiting line using Queuing Theory at cooperative-mart. Until today, no research conducted about school cooperatives-mart to observe and solve the massive inflow of customers at lines at a given time especially during lunch hour. The purposes of this study are to determine the customer congestion at the payment counter and to propose the effective queuing system at Cooperative-mart. Waiting and services times of customers at cooperative-mart is studied in three times period that to be considered as peak hours in two types of counter which are for male and female.  Data collection was observed by using queuing theory and the M/M/1/∞/∞ queuing model has been implemented.  The results show that for optimum service level, the counter must be changed from one to two counters each side.  The summary and finding of the study shall be used as guideline for the management of cooperative-mart in deciding improvement of its operation. 

A note on networks of infinite-server queues

1981 ◽  
Vol 18 (2) ◽  
pp. 561-567 ◽  
Author(s):  
J. Michael Harrison ◽  
Austin J. Lemoine
Keyword(s):  
Steady State ◽  
Open Systems ◽  
External Source ◽  
Time Dependent ◽  
Infinite Server ◽  
Closed Systems ◽  
The Subject ◽  
Infinite Server Queues ◽  
Number Of Customers ◽  
Networks Of Queues

The subject of this paper is networks of queues with an infinite number of servers at each node in the system. Our purpose is to point out that independent motions of customers in the system, which are characteristic of infinite-server networks, lead in a simple way to time-dependent distributions of state, and thence to steady-state distributions; moreover, these steady-state distributions often exhibit an invariance with regard to distributions of service in the network. We consider closed systems in which a fixed and finite number of customers circulate through the network and no external arrivals or departures are permitted, and open systems in which customers originate from an external source according to a Poisson process, possibly non-homogeneous, and each customer eventually leaves the system.

scholarly journals PENERAPAN METODE ANTRIAN DALAM MENENTUKAN FASILITAS YANG OPTIMAL PADA SPBU MAWADDAH

2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Diana Khairani Sofyan ◽  
Sri Meutia
Keyword(s):  
Diesel Fuel ◽  
Arrival Rate ◽  
Service Level ◽  
Service Rate ◽  
Queuing Model ◽  
Fuel Pump ◽  
Gas Stations ◽  
Scenario Design ◽  
Service Rates ◽  
Number Of Customers

Gas stations Mawaddah Is one of the gas stations located in the Village Batuphat East Lhokseumawe. The gas station has 5 oil pumps consisting of premium with two pumps, diesel consists of two pumps, and pertamax consists of one pump. Preliminary data have been made regarding the arrival rate of vehicles in each pump, which is a two-wheeled premium filling pump of 195 vehicles, four or more 166 wheels or four wheels filling pumps, four or more diesel fuel pumps of 156 and a feeding pump of 138 vehicles. High vehicle arrival rate resulted in queue. To calculate the level of service has never been done so it is not known the maximum time for service on each pump. The research method used is queuing model related to arrival rate and service level, with result of research which obtained is vehicle arrival rate at each pump that is 2 wheel of premium gasoline pump is 2.59 minutes. The premium 4 wheels charging pump is 6.98. The 4 wheelers diesel fuel pump is 5.97 minutes and the first charging pump is 6.65 minutes with the facility number 1. Vehicle service rates of premium 2 and 4 wheelers are 15.52 minutes and 14.11 minutes, 4 wheel diesel fuel pump is 14.21 minutes and the first feed pump is 13.55 minutes with scenario design on each pump is Scenario 1 with 2 pumps, Probability of medium system empty 0.87500, Number of subscribers in the system and number of customers waiting in the queue of each 1 customer, the average customer time in the system 0.06696 minutes and waiting time as long as the customer in the queue 0.00030 minutes.Keywords: Queue, facility, arrival rate, service rate.

scholarly journals Analisis Sistem Antrian Di PT. Bank Negara Indonesia (Persero) Tbk. Kantor Cabang Manado

d'CARTESIAN ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Ripit Budiman ◽  
Djoni Hatidja ◽  
Marline S Paendong
Keyword(s):  
Data Collection ◽  
Characteristic Length ◽  
Arrival Rate ◽  
System Model ◽  
Queuing Model ◽  
Multiple Channel ◽  
Query System ◽  
Branch Office ◽  
S Model ◽  
Number Of Customers

The purpose of this research was to determine the queuing model and determine the characteristic length of the queue at PT. Bank Negara Indonesia Branch Office of Manado. Data collection was carried out for 5 days. The queuing system model used is the B:M/M/S Model (Multiple Channel Query System), the number of tellers that operating there are 7 tellers and the service used is First Come First Served, the arrival rate is Poisson distribution, and the service time is Exponential distribution. The result of this research shows the average number of arrivals is 42 customers who come per hour, and the average number of customers served is 9 customers served per hour. There are no customers in the 0.01 system, the average number of customers in the system is 6 customers, the time customers spend in the queue and is being served is 8-9 minutes, the average number of customers waiting in the queue to be served is 1 customer, and the average time of customers spend for waiting in the queue is 1-2 minutes.

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