Performance evaluation and analysis of a discrete queue system with multiple working vacations and non-preemptive priority

Zhanyou Ma; Wenbo Wang; Linmin Hu

doi:10.3934/jimo.2018196

Article Contents

2020, Volume 16, Issue 3: 1135-1148. Doi: 10.3934/jimo.2018196

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Performance evaluation and analysis of a discrete queue system with multiple working vacations and non-preemptive priority

School of Science, Yanshan University, Qinhuangdao 066004, China

^*Corresponding author
^*Corresponding author

Received: September 30, 2017

Revised: March 31, 2018

Early access: December 2018

Published: March 2020

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Abstract

In this paper, we introduce a discrete time Geo/Geo/1 queue system with non-preemptive priority and multiple working vacations. We assume that there are two types of customers in this queue system named "Customers of type-Ⅰ" and "Customers of type-Ⅱ". Customer of type-Ⅱ has a higher priority with non-preemption than Customer of type-Ⅰ. By building a discrete time four-dimensional Markov Chain which includes the numbers of customers with different priorities in the system, the state of the server and the service state, we obtain the state transition probability matrix. Using a birth-and-death chain and matrix-geometric method, we deduce the average queue length, the average waiting time of the two types of customers, and the average busy period of the system. Then, we provide some numerical results to evaluate the effect of the parameters on the system performance. Finally, we develop some benefit functions to analyse both the personal and social benefit, and obtain some optimization results within a certain range.

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Mathematics Subject Classification: Primary: 60K25; Secondary: 90B22.

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Figure 1. Schematic illustration for the service process of the non-preemptive priority queue

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Figure 2. Schematic diagram for the model description

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Figure 3. Relation of $ E(L_1) $ with $ \lambda $ and $ \alpha $

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Figure 4. Relation of $ E(L_2) $ with $ \lambda $ and $ \alpha $

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Figure 5. Relation of $ E(L) $ with $ \theta $ and $ \mu_1 $

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Figure 6. Relation of $ E(B) $ with $ \mu _1 $ and $ \mu_2 $

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Figure 7. Relation of $ B_1 $ with $ \lambda $ and $ \alpha $

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Figure 8. Relation of $ B_2 $ with $ \lambda $ and $ \alpha $

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Figure 9. Relation of $ D $ with $ \mu _1 $ and $ \alpha $

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Figure 10. Relation of $ D $ with $ \lambda $ and $ \alpha $

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Table 1. Relation of $ E(W_1) $ with $ \mu _1 $ and $ \mu _2 $

$ \mu _2 $	$ \mu _1 =0.30 $	$ \mu _1 =0.32 $	$ \mu _1 =0.34 $	$ \mu _1 =0.36 $	$ \mu _1 =0.38 $	$ \mu _1 =0.40 $
0.45	4.7081	2.9925	2.1313	1.6271	1.3023	1.0787
0.50	2.5403	1.7894	1.3538	1.0748	0.8836	0.7459
0.55	1.7483	1.2914	1.0067	0.8156	0.6800	0.5797

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Table 2. Relation of $ E(W_2) $ with $ \mu _1 $ and $ \mu _2 $

$ \mu _2 $	$ \mu _1 =0.30 $	$ \mu _1 =0.32 $	$ \mu _1 =0.34 $	$ \mu _1 =0.36 $	$ \mu _1 =0.38 $	$ \mu _1 =0.40 $
0.45	3.6164	2.9022	2.3961	2.0311	1.7626	1.5610
0.50	2.4961	1.9948	1.6473	1.4001	1.2199	1.0853
0.55	1.8492	1.4802	1.2263	1.0464	0.9154	0.8175

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