Stochastic decomposition in retrial queueing-inventory system

2020 ◽  
Vol 54 (1) ◽  
pp. 81-99 ◽  
Author(s):  
Dhanya Shajin ◽  
A. Krishnamoorthy
Keyword(s):  
Joint Distribution ◽  
Storage System ◽  
Retrial Queue ◽  
Form Solution ◽  
Inventory System ◽  
Product Form ◽  
Stochastic Decomposition ◽  
Retrial Queueing ◽  
Long Run ◽  
Number Of Customers

The purpose of this paper is to obtain product form solution for retrial – queueing – inventory system. We study an M/M/1 retrial queue with a storage system driven by an (s,S) policy. When server is idle, external arrivals enter directly to an orbit. Inventory replenishment lead time is exponentially distributed. The interval between two successive retrials is exponentially distributed and only the customer at the head of the orbit is permitted to access the server. No customer is allowed to join the orbit when the storage system is empty and also when the serer is busy. We first derive the stationary joint distribution of the queue length and the on-hand inventory in explicit product form. Using the joint distribution, we investigate long-run performance measures such as distribution of number of customers served, number of arrivals, number of customers lost during an interval of random duration and a cost function. The optimal pair (s,S) is numerically investigated.

scholarly journals Analysis of a Multiserver Queueing-Inventory System

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
A. Krishnamoorthy ◽  
R. Manikandan ◽  
Dhanya Shajin
Keyword(s):  
Steady State ◽  
Service Time ◽  
Conditional Distribution ◽  
Retrial Queue ◽  
Form Solution ◽  
Inventory System ◽  
Inventory Level ◽  
Steady State Distribution ◽  
State Distribution ◽  
Number Of Customers

We attempt to derive the steady-state distribution of theM/M/cqueueing-inventory system with positive service time. First we analyze the case ofc=2servers which are assumed to be homogeneous and that the service time follows exponential distribution. The inventory replenishment follows the(s,Q)policy. We obtain a product form solution of the steady-state distribution under the assumption that customers do not join the system when the inventory level is zero. An average system cost function is constructed and the optimal pair(s,Q)and the corresponding expected minimum cost are computed. As in the case ofM/M/cretrial queue withc≥3, we conjecture thatM/M/cforc≥3, queueing-inventory problems, do not have analytical solution. So we proceed to analyze such cases using algorithmic approach. We derive an explicit expression for the stability condition of the system. Conditional distribution of the inventory level, conditioned on the number of customers in the system, and conditional distribution of the number of customers, conditioned on the inventory level, are derived. The distribution of two consecutivestostransitions of the inventory level (i.e., the first return time tos) is computed. We also obtain several system performance measures.

scholarly journals Sojourn Times in A Queueing System with Breakdowns and General Retrial Times

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2882
Author(s):  
Ivan Atencia ◽  
José Luis Galán-García
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Queueing System ◽  
Retrial Queue ◽  
Stochastic Decomposition ◽  
Discrete Time System ◽  
Main Research ◽  
Extensive Analysis ◽  
Complete Study ◽  
Number Of Customers

This paper centers on a discrete-time retrial queue where the server experiences breakdowns and repairs when arriving customers may opt to follow a discipline of a last-come, first-served (LCFS)-type or to join the orbit. We focused on the extensive analysis of the system, and we obtained the stationary distributions of the number of customers in the orbit and in the system by applying the generation function (GF). We provide the stochastic decomposition law and the application bounds for the proximity between the steady-state distributions for the queueing system under consideration and its corresponding standard system. We developed recursive formulae aimed at the calculation of the steady-state of the orbit and the system. We proved that our discrete-time system approximates M/G/1 with breakdowns and repairs. We analyzed the busy period of an auxiliary system, the objective of which was to study the customer’s delay. The stationary distribution of a customer’s sojourn in the orbit and in the system was the object of a thorough and complete study. Finally, we provide numerical examples that outline the effect of the parameters on several performance characteristics and a conclusions section resuming the main research contributions of the paper.

scholarly journals A Discrete-TimeGeo/G/1 Retrial Queue with Two Different Types of Vacations

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Feng Zhang ◽  
Zhifeng Zhu
Keyword(s):  
Discrete Time ◽  
Queueing System ◽  
Retrial Queue ◽  
System Size ◽  
Stochastic Decomposition ◽  
Continuous Time Model ◽  
Time Model ◽  
Different Types ◽  
Number Of Customers ◽  
The Relationship

We analyze a discrete-timeGeo/G/1 retrial queue with two different types of vacations and general retrial times. Two different types of vacation policies are investigated in this model, one of which is nonexhaustive urgent vacation during serving and the other is normal exhaustive vacation. For this model, we give the steady-state analysis for the considered queueing system. Firstly, we obtain the generating functions of the number of customers in our model. Then, we obtain the closed-form expressions of some performance measures and also give a stochastic decomposition result for the system size. Moreover, the relationship between this discrete-time model and the corresponding continuous-time model is also investigated. Finally, some numerical results are provided to illustrate the effect of nonexhaustive urgent vacation on some performance characteristics of the system.

scholarly journals An M/G/1 Retrial Queueing System with Phase Type Services and Working Vacations

2018 ◽  
Vol 7 (4.10) ◽  
pp. 758
Author(s):  
P. Rajadurai ◽  
R. Santhoshi ◽  
G. Pavithra ◽  
S. Usharani ◽  
S. B. Shylaja
Keyword(s):  
Queueing System ◽  
Retrial Queue ◽  
Probability Generating Function ◽  
Retrial Queueing System ◽  
Retrial Queueing ◽  
Multiple Working Vacations ◽  
Phase Type ◽  
Working Vacations ◽  
Multi Phase ◽  
Number Of Customers

A multi phase retrial queue with optional re-service and multiple working vacations is considered. The Probability Generating Function (PGF) of number of customers in the system is obtained by supplementary variable technique. Various system performance measures are discussed. 

The M/G/1 Retrial Queue With Retrial Rate Control Policy

1993 ◽  
Vol 7 (1) ◽  
pp. 29-46 ◽  
Author(s):  
Bong Dae Choi ◽  
Kyung Hyune Rhee ◽  
Kwang Kyu Park
Keyword(s):  
Retrial Queue ◽  
Control Policy ◽  
Single Server ◽  
Retrial Queueing System ◽  
Retrial Queueing ◽  
Virtual Waiting Time ◽  
The Laplace Transform ◽  
The Stability ◽  
Necessary And Sufficient ◽  
Number Of Customers

We consider a single-server retrial queueing system where retrial time is inversely proportional to the number of customers in the system. A necessary and sufficient condition for the stability of the system is found. We obtain the Laplace transform of virtual waiting time and busy period. The transient distribution of the number of customers in the system is also obtained.

scholarly journals Analysis of an M/PH/1 Retrial Queueing-Inventory System with Level Dependent Retrial Rate

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zaiming Liu ◽  
Xuxiang Luo ◽  
Jinbiao Wu
Keyword(s):  
Life Time ◽  
Maximum Size ◽  
Inventory System ◽  
Phase Type Distribution ◽  
Steady State Probability ◽  
Numerical Illustration ◽  
Retrial Queueing ◽  
Common Life Time ◽  
The Stability ◽  
Number Of Customers

We analyze a queueing-inventory system which can model airline and railway reservation systems. An arriving customer to an idle server joins for service immediately with exactly one item from inventory at the moment of service completion if there are some on-hand inventory, or else he accesses to a buffer of varying size (the buffer capacity varies and equals to the number of the items in the inventory with maximum size S). When the buffer overflows, the customer joins an orbit of infinite capacity with probability p or is lost forever with probability 1−p. Arrivals form a Poisson process, and service time has phase type distribution. The time between any two successive retrials of the orbiting customer is exponentially distributed with parameter depending on the number of customers in the orbit. In addition, the items have a common life time with exponentially distributed. Cancellation of orders is possible before their expiry and intercancellation times are assumed to be exponentially distributed. The stability condition and steady-state probability vector have been studied by Neuts–Rao truncation method using the theory of Level Dependent Quasi-Birth-Death (LDQBD) processes. Several stationary performance measures are also computed. Furthermore, we provide numerical illustration of the system performance with variation in values of underlying parameters and analyze an optimization problem.

scholarly journals On the single-server retrial queue

2006 ◽  
Vol 16 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Natalia Djellab
Keyword(s):  
Approximate Solution ◽  
Retrial Queue ◽  
Retrial Queues ◽  
Stochastic Decomposition ◽  
Single Server ◽  
Queue Size ◽  
Decomposition Property ◽  
The Subject ◽  
Exponential Case ◽  
Number Of Customers

In this work, we review the stochastic decomposition for the number of customers in M/G/1 retrial queues with reliable server and server subjected to breakdowns which has been the subject of investigation in the literature. Using the decomposition property of M/G/1 retrial queues with breakdowns that holds under exponential assumption for retrial times as an approximation in the non-exponential case, we consider an approximate solution for the steady-state queue size distribution.

Stochastic analysis of a preemptive retrial queue with orbital search and multiple vacations

2020 ◽  
Vol 54 (1) ◽  
pp. 231-249
Author(s):  
Shan Gao ◽  
Jinting Wang
Keyword(s):  
Cost Optimization ◽  
Retrial Queue ◽  
Length Distribution ◽  
Stochastic Decomposition ◽  
Embedded Markov Chain ◽  
Multiple Vacations ◽  
Retrial Queueing System ◽  
Queue Length Distribution ◽  
Retrial Queueing ◽  
Necessary And Sufficient

This paper deals with a preemptive priority M/G/1 retrial queue with orbital search and exhaustive multiple vacations. By using embedded Markov chain technique and the supplementary variable method, we discuss the necessary and sufficient condition for the system to be stable and the joint queue length distribution in steady state as well as some important performance measures and the Laplace–Stieltjes transform of the busy period. Also, we establish a special case and the stochastic decomposition laws for this preemptive retrial queueing system. Finally, some numerical examples and cost optimization analysis are presented.

scholarly journals Performance evaluation of two Markovian retrial queueing model with balking and feedback

2013 ◽  
Vol 5 (2) ◽  
pp. 132-146 ◽  
Author(s):  
A. A. Bouchentouf ◽  
F. Belarbi
Keyword(s):  
Performance Evaluation ◽  
Queue Length ◽  
Joint Distribution ◽  
Queueing System ◽  
Retrial Queue ◽  
Queueing Model ◽  
Retrial Queueing System ◽  
Retrial Queueing ◽  
Queue Lengths ◽  
The Relationship

Abstract In this paper, we consider the performance evaluation of two retrial queueing system. Customers arrive to the system, if upon arrival, the queue is full, the new arriving customers either move into one of the orbits, from which they make a new attempts to reach the primary queue, until they find the server idle or balk and leave the system, these later, and after getting a service may comeback to the system requiring another service. So, we derive for this system, the joint distribution of the server state and retrial queue lengths. Then, we give some numerical results that clarify the relationship between the retrials, arrivals, balking rates, and the retrial queue length.

Sign in / Sign up

Export Citation Format

Share Document