Abstract
This paper considers a discrete-time Geo/G/1 queue in a multi-phase service environment, where the system is subject to disastrous breakdowns, causing all present customers to leave the system simultaneously. At a failure epoch, the server abandons the service and the system undergoes a repair period. After the system is repaired, it jumps to operative phase i with probability qi, i = 1, 2 ⋯, n. Using the supplementary variable technique, we obtain the distribution for the stationary queue length at the arbitrary epoch, which are then used for the computation of other performance measures. In addition, we derive the expected length of a cycle time, the generating function of the sojourn time of an arbitrary customer, and the generating function of the server’s working time in a cycle. We also give the relationship between the discrete-time queueing system to its continuous-time counterpart. Finally, some examples and numerical results are presented.
References
[1] Yechiali U. Queues with system disasters and impatient customers when system is down. Queueing Systems, 2007, 56: 195–202.10.1007/s11134-007-9031-zSearch in Google Scholar
[2] Giorno V, Nobile A G, Spina S. On some time non-homogeneous queueing systems with catastrophes. Applied Mathematics and Computation, 2014, 245: 220–234.10.1016/j.amc.2014.07.076Search in Google Scholar
[3] Paz N, Yechiali U. An M/M/1 queue in random environment with disasters. Asia-Pacific Journal of Operational Research, 2014, 31(3). 10.1142/S021759591450016X.Search in Google Scholar
[4] Mytalas G C, Zazanis M A. An M[x]/G/1 queueing system with disasters and repairs under a multiple adapted vacation policy. Naval Research Logistics, 2015, 62: 171–18910.1002/nav.21621Search in Google Scholar
[5] Sudhes R, Sebasthi Priya R, Lenin R B. Analysis of N-policy queues with disastrous breakdown. TOP, 2016, 24: 612–634.10.1007/s11750-016-0411-6Search in Google Scholar
[6] Kapodistria S, Phung-Duc T, Resing J. Linear birth/immigration-death process with binomial catastrophes. Probability in the Engineering and Informational Sciences, 2016, 30(1): 79–111.10.1017/S0269964815000297Search in Google Scholar
[7] Jiang T, Liu L. Analysis of a GI/M/1 in a multi-phase service environment with disasters. RAIRO — Operations Research, 2017, 51: 79–100.10.1051/ro/2016005Search in Google Scholar
[8] Jiang T, Liu L. The GI/M/1 queue in a multi-phase service environment with disasters and working breakdowns. International Journal of Computer Mathematics, 2017, 94(4): 707–726.10.1080/00207160.2015.1128531Search in Google Scholar
[9] Zhang X, Liu L, Jiang T. Analysis of an M/G/1 stochastic clearing queue in a 3-Phase Environment. Journal of Systems Science and Information, 2015, 3(4): 374–384.10.1515/JSSI-2015-0374Search in Google Scholar
[10] Ye J, Liu L, Jiang T. Analysis of a single-sever queue with disasters and repairs under Bernoulli vacation schedule. Journal of Systems Science and Information, 2016, 4(6): 547–559.10.21078/JSSI-2016-547-13Search in Google Scholar
[11] Atencia I, Moreno P. The discrete-time Geo/Geo/1 queue with negative customers and disasters. Computers & Operations Research, 2004, 31: 1537–1548.10.1016/S0305-0548(03)00107-2Search in Google Scholar
[12] Yi X W, Kim J D, Choi D W, et al. The Geo/G/1 queue with disasters and multiple working vacations. Stochastic Models, 2007, 23: 21–31.10.1080/15326340701645926Search in Google Scholar
[13] Park H M, Yang W S, Chae K C. The Geo/G/1 with negative customers and disasters. Stochastic Models, 2009, 25: 673–688.10.1080/15326340903291347Search in Google Scholar
[14] Park H M, Yang W S, Chae K C. Analysis of the GI/Geo/1 queue with disasters. Stochastic Analyis and Applications, 2010, 28: 44–53.10.1080/07362990903417938Search in Google Scholar
[15] Lee D H, Yang W S, Park H M. Geo/G/1 queues with disasters and general repair times. Applied Mathematical Modelling, 2011, 35: 1561–1570.10.1016/j.apm.2010.09.032Search in Google Scholar
[16] Lee D H, Yang W S. The N-policy of a discrete time Geo/G/1 queue with disasters and its application to wireless sensor networks. Applied Mathematical Modelling, 2013, 37: 9722–9731.10.1016/j.apm.2013.05.012Search in Google Scholar
[17] Atencia I, Moreno P. A Discrete-Time Geo/G/1 retrial queue with the server subject to starting failures. Annals of Operations Research, 2006, 141: 85–107.10.1007/s10479-006-5295-7Search in Google Scholar
[18] Yang T, Li H. On the steady-state queue size distribution of the discrete-time Geo/G/1 queue with repeated customers. Queueing Systems, 1995, 21: 199–215.10.1007/BF01158581Search in Google Scholar
© 2018 Walter De Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting
- Application of Drone in Solving Last Mile Parcel Delivery
- The Difference of Capital Input and Productivity in Service Industries: Based on Four Stages Bootstrap-DEA Model
- Parameter Estimation of a Mixed Production Function Model Based on Improved Firefly Algorithm and Model Application
- Analysis of a Discrete-Time Geo/G/1 Queue in a Multi-Phase Service Environment with Disasters
- A New K-Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks
- Non-equidistance DGM(1,1) Model Based on the Concave Sequence and Its Application to Predict the China’s Per Capita Natural Gas Consumption
Articles in the same Issue
- A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting
- Application of Drone in Solving Last Mile Parcel Delivery
- The Difference of Capital Input and Productivity in Service Industries: Based on Four Stages Bootstrap-DEA Model
- Parameter Estimation of a Mixed Production Function Model Based on Improved Firefly Algorithm and Model Application
- Analysis of a Discrete-Time Geo/G/1 Queue in a Multi-Phase Service Environment with Disasters
- A New K-Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks
- Non-equidistance DGM(1,1) Model Based on the Concave Sequence and Its Application to Predict the China’s Per Capita Natural Gas Consumption