Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Transient Behavior of a Single-Server Markovian Queue with Balking and Working Vacation Interruptions

  • Published:
Journal of the Operations Research Society of China Aims and scope Submit manuscript

Abstract

This paper studies the time-dependent analysis of an M/M/1 queueing model with single, multiple working vacation, balking and vacation interruptions. Whenever the system becomes empty, the server commences working vacation. During the working vacation period, if the queue length reaches a positive threshold value ‘k’, the working vacation of the server is interrupted and it immediately starts the service in an exhaustive manner. During working vacations, the customers become discouraged due to the slow service and possess balking behavior. The transient system size probabilities of the proposed model are derived explicitly using the method of generating function and continued fraction. The performance indices such as average and variance of system size are also obtained. Further, numerical simulations are presented to analyze the impact of system parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Servi, L.D., Finn, S.G.: M/M/1 queue with working vacations (M/M/1/WV). Perform. Eval. 50, 41–52 (2002)

    Article  Google Scholar 

  2. Sudhesh, R., Azhagappan, A., Dharmaraja, S.: Transient analysis of M/M/1 queue with working vacation heterogeneous service and customers’ impatience. RAIRO Oper. Res. 51(3), 591–606 (2017)

    Article  MathSciNet  Google Scholar 

  3. Sudhesh, R., Azhagappan, A.: Transient analysis of an M/M/1 queue with variant impatient behavior and working vacations. Opsearch 55(3–4), 787–806 (2018)

    Article  MathSciNet  Google Scholar 

  4. Tian, N., Zhao, X., Wang, K.: The M/M/1 queue with single working vacation. Int. J. Inf. Manag. Sci. 19, 621–634 (2008)

    MathSciNet  MATH  Google Scholar 

  5. Vijayalaxmi, P., Jyothsna, K.: Analysis of finite buffer renewal input queue with balking and multiple working vacations. Opsearch 50, 548–565 (2013)

    Article  MathSciNet  Google Scholar 

  6. Haight, F.A.: Queueing with balking. Biometrika 44, 360–369 (1957)

    Article  MathSciNet  Google Scholar 

  7. Kumar, B.K., Parthasarthy, P.R., Sharafali, M.: Transient solution of an M/M/1 queue with balking. Queueing Syst. 13(4), 441–447 (1993)

    Article  MathSciNet  Google Scholar 

  8. Baba, Y.: The M/PH/1 queue with working vacations and vacation interruption. J. Syst. Sci. Syst. Eng. 19(4), 496–503 (2010)

    Article  Google Scholar 

  9. Gao, S., Liu, Z.: An M/G/1 queue with single working vacation and vacation interruption under Bernoulli schedule. Appl. Math. Model. 37(3), 1564–1579 (2013)

    Article  MathSciNet  Google Scholar 

  10. Goswami, C., Selvaraju, N.: A working vacation queue with priority customers and vacation interruptions. Int. J. Oper. Res. 17(3), 311–332 (2013)

    Article  MathSciNet  Google Scholar 

  11. Isijola-Adakeja, O.A., Ibe, O.C.: M/M/1 multiple vacation queueing systems with differentiated vacations and vacation interruptions. IEEE Access 2, 1384–1395 (2014)

    Article  Google Scholar 

  12. Li, J., Tian, N.: The M/M/1 queue with working vacations and vacation interruptions. J. Syst. Sci. Syst. Eng. 16, 121–127 (2007)

    Article  Google Scholar 

  13. Zhao, G., Du, X., Tian, N.: GI/M/1 queue with set-up period and working vacation and vacation interruption. Int. J. Inf. Manag. Sci. 20, 351–363 (2009)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thirunavukkarasu Deepa.

Appendices

Appendix A

Proof of Theorem 1

Let

$$\begin{aligned} Q(z,\tau )= & {} \sum \limits _{n=1}^{\infty }\pi _{n,1}(\tau )z^n. \end{aligned}$$
(7.1)

Using (7.1) in Eqs. (2.2), (2.3) and (2.4), we obtain

$$\begin{aligned} \frac{\partial Q (z,\tau )}{\partial \tau }= & {} \left[ -(\lambda _{1}+ \mu _{1})+\lambda _{1}z+\frac{ \mu _{1} }{z} \right] Q (z,\tau )+\eta \sum \limits _{n=1}^{k-1}\pi _{n,0}(\tau )z^n\nonumber \\&+\,\sigma \lambda _{0}\pi _{k-1,0}(\tau )z^{k}+\lambda _{1}z\pi _{0,1}(\tau )- \mu _{1} \pi _{1,1}(\tau ). \end{aligned}$$

Solving the above partial differential equation, we get

$$\begin{aligned} Q(z,\tau )= & {} \eta \int \limits _{0}^{\tau }\left[ \sum \limits _{n=1}^{k-1}\pi _{n,0}(\tau )z^n\right] \mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) } \mathrm{e}^{\left( \lambda _{1} z+ \frac{ \mu _{1}}{z}\right) (\tau -u) }\hbox {d}u \nonumber \\&+\,\sigma \lambda _{0}\int \limits _{0}^{\tau }\pi _{k-1,0}(u)z^{k}\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\mathrm{e}^{(\lambda _{1} z+ \frac{ \mu _{1}}{z})(\tau -u) }\hbox {d}u \nonumber \\&+\,\lambda _{1} \int \limits _{0}^{\tau }\pi _{0,1}(u)z\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\mathrm{e}^{(\lambda _{1} z+ \frac{ \mu _{1}}{z})(\tau -u) }\hbox {d}u\nonumber \\&-\,\mu _{1} \int \limits _{0}^{\tau }\pi _{1,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\mathrm{e}^{(\lambda _{1} z+ \frac{ \mu _{1}}{z})(\tau -u) }\hbox {d}u. \end{aligned}$$
(7.2)

Let

$$\begin{aligned} \mathrm{e}^{(\lambda _{1} z+ \frac{ \mu _{1}}{z})\tau }= & {} \sum \limits _{-\infty \leqslant n \leqslant \infty }^{}(\beta z)^n I_{n}(\alpha \tau ). \end{aligned}$$
(7.3)

Using (7.3) in (7.2) and simplifying for \(n\geqslant 1\), we get

$$\begin{aligned} \pi _{n,1}(\tau )&= \eta \int \limits _{0}^{\tau }\left[ \sum \limits _{m=1}^{k-1}\pi _{m,0}(u)\right] \mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\beta ^{n-m} I_{n-m}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad +\sigma \lambda _{0}\int \limits _{0}^{\tau }\pi _{k-1,0}(u)\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\beta ^{n-k}I_{n-k}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad +\lambda _{1}\int \limits _{0}^{\tau }\pi _{0,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\beta ^{n-1}I_{n-1}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad -\mu _{1}\int \limits _{0}^{\tau }\pi _{1,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\beta ^{n}I_{n}(\alpha (\tau -u))\hbox {d}u. \end{aligned}$$
(7.4)

The above expression holds for \(n = -1, -2, -3, \cdots \). Using \( I_{-n}(y)= I_{n}(y)\), for \(n \geqslant 1,\) we have

$$\begin{aligned} 0&=\eta \int \limits _{0}^{\tau }\sum \limits _{m=1}^{k-1}\pi _{m,0}(u) \mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{-n-m} I_{n+m}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad +\sigma \lambda _{0}\int \limits _{0}^{\tau }\pi _{k-1,0}(u)\mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{-n-k}I_{n+k}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad +\lambda _{1}\int \limits _{0}^{\tau }\pi _{0,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{-n-1}I_{n+1}(\alpha (\tau -u))\hbox {d}u\nonumber \\&\quad -\mu \int \limits _{0}^{\tau }\pi _{1,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{-n}I_{n}(\alpha (\tau -u))\hbox {d}u. \end{aligned}$$
(7.5)

From (7.4) and (7.5), for \(n \geqslant 1\), we obtain (2.8). Taking Laplace transform of (2.1), we get

$$\begin{aligned} \hat{\pi }_{0,1}(s)=&\left( \frac{\eta }{s+ \lambda _{1}}\right) \hat{\pi }_{0,0}(s). \end{aligned}$$
(7.6)

On Laplace inversion of (7.6), we obtain (2.9).

Appendix B

Proof of Theorem 2

Taking Laplace transform of (2.7), we get

$$\begin{aligned} \hat{\pi }_{k-1,0}(s)=&\left( \frac{\sigma \lambda _{0}}{s+\sigma \lambda _{0}+\mu _{0}+\eta } \right) \hat{\pi }_{k-2,0}(s). \end{aligned}$$
(7.7)

On Laplace inversion of (7.7), we obtain (2.10).

Taking Laplace transform of (2.6), for \( 1 \leqslant n \leqslant k-2\), we get

$$\begin{aligned} \frac{\hat{\pi }_{n,0}(s)}{\hat{\pi }_{n-1,0}(s)}=&\frac{\sigma \lambda _{0}}{(s+\sigma \lambda _{0}+\mu _{0}+\eta )-\mu _{0}\frac{\hat{\pi }_{n+1,0}(s)}{\hat{\pi }_{n,0}(s)}}. \end{aligned}$$

Iterating the above equation, for \( 1 \leqslant n \leqslant k-2\), we get

$$\begin{aligned} \hat{\pi }_{n,0}(s)=&\beta _{0}^{n} \left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{n}\hat{\pi }_{0,0}(s), \end{aligned}$$
(7.8)

where \(w_{0}=s+\sigma \lambda _{0}+\mu _{0}+\eta \). Laplace inversion of (7.8) leads to (2.11).

Taking Laplace transform of (2.8), for \(n=1\), and using (7.6), (7.7) and (7.8), we get

$$\begin{aligned} \hat{\pi }_{1,1} (s)=&\hat{A}(s)\hat{\pi }_{0,0}(s), \end{aligned}$$
(7.9)

where

$$\begin{aligned} \hat{A}(s)&=\frac{\eta }{\lambda _{1}} \sum \limits _{m=1}^{k-2}\beta _{0}^{m}\beta _{1}^{2-m}\left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{m}\left( \frac{w_{1}-\sqrt{w_{1}^{2}-\alpha _{1}^{2}}}{\alpha _{1}} \right) ^{m}\\&\quad +\frac{\eta }{\lambda _{1}}\beta _{0}^{k-2}\beta _{1}^{3-k} \left( \frac{ \sigma \lambda _{0}}{s+\sigma \lambda _{0}+\mu _{0}+\eta }\right) \\&\quad \left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\sigma \alpha _{0}} \right) ^{k-2}\left( \frac{w_{1}-\sqrt{w_{1}^{2}-\alpha _{1}^{2}}}{\alpha _{1}} \right) ^{k-1}\\&\quad +\beta _{1}\left( \frac{ \eta }{s+\lambda _{1}}\right) \left( \frac{w_{1}-\sqrt{w_{1}^{2}-\alpha _{1}^{2}}}{\alpha _{1}} \right) \\&\quad +\frac{(\sigma \lambda _{0})^{2}}{\lambda _{1}}\beta _{0}^{k-2}\beta _{1}^{2-k} \left( \frac{ 1}{s+\sigma \lambda _{0}+\mu _{0}+\eta }\right) \\&\quad \left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{k-2}\left( \frac{w_{1}-\sqrt{w_{1}^{2}-\alpha _{1}^{2}}}{\alpha _{1}} \right) ^{k}. \end{aligned}$$

Laplace transform of (2.5) yields

$$\begin{aligned} \hat{\pi }_{0,0}(s)=&\frac{1}{(s+\sigma \lambda _{0}+\eta )-\mu _{0}\frac{\hat{\pi }_{1,0}(s)}{\hat{\pi }_{0,0}(s)}-\mu _{1}\frac{\hat{\pi }_{1,1}(s)}{\hat{\pi }_{0,0}(s)}}. \end{aligned}$$
(7.10)

Using (7.8), for \(n=1\), and (7.9) in (7.10), we get

$$\begin{aligned} \hat{\pi }_{0,0}(s)=&\sum \limits _{k=0}^{\infty }\sum \limits _{j=0}^{k}\left( {\begin{array}{c}{k} \\ j \\ \end{array}} \right) \frac{(\mu _{0}\beta _{0})^{j}\mu _{1}^{k-j}{\hat{A}(s)}^{k-j}}{(s+\sigma \lambda _{0}+\eta )^{k+1}}\left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{j}. \end{aligned}$$
(7.11)

On Laplace inversion of (7.11), we obtain (2.12). It is difficult to derive the steady-state distributions from the transient counterpart as the results in time-dependent state are obtained as finite and infinite summations.

Appendix C

Proof of Theorem 3

Applying (7.1) in Eqs. (3.1) to (3.4), we obtain

$$\begin{aligned} \frac{\partial Q (z,\tau )}{\partial \tau }&=\left[ -(\lambda _{1}+ \mu _{1})+\lambda _{1}z+\frac{ \mu _{1} }{z} \right] Q (z,\tau )\\&\quad +\eta \sum \limits _{n=1}^{k-1}\pi _{n,0}(\tau )z^n+\sigma \lambda _{0}\pi _{k-1,0}(\tau )z^{k-1}- \mu _{1} \pi _{1,1}(\tau ). \end{aligned}$$

Solving the above partial differential equation, we obtain

$$\begin{aligned} Q (s,\tau )&=\eta \int \limits _{0}^{\tau }\left[ \sum \limits _{n=1}^{k-1}\pi _{n,0}(\tau )z^n\right] \mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) } \mathrm{e}^{\left( \lambda _{1} z+ \frac{ \mu _{1}}{z}\right) (\tau -u) }\hbox {d}u \nonumber \\&\quad +\sigma \lambda _{0}\int \limits _{0}^{\tau }\pi _{k-1,0}(u)z^{k-1}\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\mathrm{e}^{(\lambda _{1} z+ \frac{ \mu _{1}}{z})(\tau -u) }\hbox {d}u\nonumber \\&\quad -\mu _{1} \int \limits _{0}^{\tau }\pi _{1,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu _{1})(\tau -u) }\mathrm{e}^{(\lambda _{1} z+ \frac{ \mu _{1}}{z})(\tau -u)}\hbox {d}u. \end{aligned}$$
(7.12)

Using (7.3) in (7.12) and simplifying for \(n\geqslant 1\), we get

$$\begin{aligned} \pi _{n,1}(\tau )&= \eta \int \limits _{0}^{\tau }\left[ \sum \limits _{m=1}^{k-1}\pi _{m,0}(u)\right] \mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{n-m} I_{n-m}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad +\sigma \lambda _{0}\int \limits _{0}^{\tau }\pi _{k-1,0}(u)\mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{n-k+1}I_{n-k+1}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad -\mu \int \limits _{0}^{\tau }\pi _{1,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{n}I_{n}(\alpha (\tau -u))\hbox {d}u. \end{aligned}$$
(7.13)

The above expression holds for \(n = -1, -2, -3, \cdots \). Using \( I_{-n}(y)= I_{n}(y)\), for \(n \geqslant 1,\) we have

$$\begin{aligned} 0&=\eta \int \limits _{0}^{\tau }\sum \limits _{m=1}^{k-1}\pi _{m,0}(u) \mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{-n-m} I_{n+m}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad +\sigma \lambda _{0}\int \limits _{0}^{\tau }\pi _{k-1,0}(u)\mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{-n-k+1}I_{n+k-1}(\alpha (\tau -u))\hbox {d}u \nonumber \\&\quad -\mu \int \limits _{0}^{\tau }\pi _{1,1}(u)\mathrm{e}^{-(\lambda _{1}+ \mu )(\tau -u) }\beta ^{-n}I_{n}(\alpha (\tau -u))\hbox {d}u. \end{aligned}$$
(7.14)

From (7.13) and (7.14), for \(n \geqslant 1\), we obtain (3.8).

Appendix D

Proof of Theorem 4

Taking Laplace transform of (3.8), for \(n=1\), and using (7.7) and (7.8), we get

$$\begin{aligned} \hat{\pi }_{1,1} (s)=&\hat{B}(s)\hat{\pi }_{0,0}(s), \end{aligned}$$
(7.15)

where

$$\begin{aligned} \hat{B}(s)&=\frac{\eta }{\lambda _{1}} \sum \limits _{m=1}^{k-2}\beta _{0}^{m}\beta _{1}^{2-m}\left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{m}\left( \frac{w_{1}-\sqrt{w_{1}^{2}-\alpha _{1}^{2}}}{\alpha _{1}} \right) ^{m}\\&\quad +\frac{\eta }{\lambda _{1}}\beta _{0}^{k-2}\beta _{1}^{3-k} \left( \frac{ \sigma \lambda _{0}}{s+\sigma \lambda _{0}+\mu _{0}+\eta }\right) \\&\quad \left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{k-2}\left( \frac{w_{1}-\sqrt{w_{1}^{2}-\alpha _{1}^{2}}}{\alpha _{1}} \right) ^{k-1}\\&\quad +\frac{(\sigma \lambda _{0})^{2}}{\lambda _{1}}\beta _{0}^{k-2}\beta _{1}^{2-k} \left( \frac{ 1}{s+\sigma \lambda _{0}+\mu _{0}+\eta }\right) \\&\quad \left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{k-2}\left( \frac{w_{1}-\sqrt{w_{1}^{2}-\alpha _{1}^{2}}}{\alpha _{1}} \right) ^{k}. \end{aligned}$$

Laplace transform of (3.5) yields

$$\begin{aligned} \hat{\pi }_{0,0}(s)=&\frac{1}{(s+\sigma \lambda _{0})-\mu _{0}\frac{\hat{\pi }_{1,0}(s)}{\hat{\pi }_{0,0}(s)}-\mu _{1}\frac{\hat{\pi }_{1,1}(s)}{\hat{\pi }_{0,0}(s)}}. \end{aligned}$$
(7.16)

Using (7.8), for \(n=1\), and (7.15) in (7.16), we get

$$\begin{aligned} \hat{\pi }_{0,0}(s)=&\sum \limits _{k=0}^{\infty }\sum \limits _{j=0}^{k}\left( {\begin{array}{c}{k} \\ j \\ \end{array}} \right) \frac{(\mu _{0}\beta _{0})^{j}\mu _{1}^{k-j}{\hat{B}(s)}^{k-j}}{(s+\sigma \lambda _{0})^{k+1}}\left( \frac{w_{0}-\sqrt{w_{0}^{2}-\alpha _{0}^{2}}}{\alpha _{0}} \right) ^{j}. \end{aligned}$$
(7.17)

On Laplace inversion of (7.17), we obtain (3.9).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Azhagappan, A., Deepa, T. Transient Behavior of a Single-Server Markovian Queue with Balking and Working Vacation Interruptions. J. Oper. Res. Soc. China 9, 321–341 (2021). https://doi.org/10.1007/s40305-019-00288-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40305-019-00288-3

Keywords

Mathematics Subject Classification