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July  2017, 13(3): 1537-1552. doi: 10.3934/jimo.2017006

Optimal threshold control of a retrial queueing system with finite buffer

School of Mathematics and Statistics, Central South University, Changsha 410083, Hunan, China

* Corresponding author: Jinbiao Wu

Received  September 2015 Published  December 2016

Fund Project: The second author is supported by the National Natural Science Foundation of China (11271373) and the third author is supported by the project of Mathematics and Interdisciplinary Science and Innovation-Driven of Central South University (10900-506010101) and the Yu Ying project of Central South University.

In this paper, we analyze the optimal control of a retrial queueing system with finite buffer K. At any decision epoch, if the buffer is full, the controller have to make two decisions: one is for the new arrivals, to decide whether they are allowed to join the orbit or not (admission control); the other one is for the repeated customers, to decide whether they are allowed to get back to the orbit or not (retrial control). The problems are constructed as a Markov decision process. We show that the optimal policy has a threshold-type structure and the thresholds are monotonic in operating parameters and various cost parameters. Furthermore, based on the structure of the optimal policy, we construct a performance evaluation model for computing efficiently the thresholds. The expression of the expected cost is given by solving the quasi-birth-and-death (QBD) process. Finally, we provide some numerical results to illustrate the impact of different parameters on the optimal policy and average cost.

Citation: Gang Chen, Zaiming Liu, Jinbiao Wu. Optimal threshold control of a retrial queueing system with finite buffer. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1537-1552. doi: 10.3934/jimo.2017006
References:
[1]

H.-S. AhnI. Duenyas and M. E. Lewis, Optimal control of a two-stage tandem queuing system with flexible servers, Probability in the Engineering and Informational Sciences, 16 (2002), 453-469.  doi: 10.1017/S0269964802164047.  Google Scholar

[2]

A. S. Alfa and K. S. Isotupa, An M/PH/k retrial queue with finite number of sources, Computers & Operations Research, 31 (2004), 1455-1464.  doi: 10.1016/S0305-0548(03)00100-X.  Google Scholar

[3]

J. R. Artalejo, Accessible bibliography on retrial queues: Progress in 2000--2009, Mathematical and Computer Modelling, 51 (2010), 1071-1081.  doi: 10.1016/j.mcm.2009.12.011.  Google Scholar

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Y. Aviv and A. Federgruen, The value iteration method for countable state markov decision processes, Operations Research Letters, 24 (1999), 223-234.  doi: 10.1016/S0167-6377(99)00015-2.  Google Scholar

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S. BenjaafarJ.-P. Gayon and S. Tepe, Optimal control of a production--inventory system with customer impatience, Operations Research Letters, 38 (2010), 267-272.  doi: 10.1016/j.orl.2010.03.008.  Google Scholar

[6]

L. Breuer, Threshold policies for controlled retrial queues with heterogeneous servers, Annals of Operations Research, 141 (2006), 139-162.  doi: 10.1007/s10479-006-5297-5.  Google Scholar

[7]

R. Cavazos-Cadena and L. I. Sennott, Comparing recent assumptions for the existence of average optimal stationary policies, Operations Research Letters, 11 (1992), 33-37.  doi: 10.1016/0167-6377(92)90059-C.  Google Scholar

[8]

E. B. ÇilF. Karaesmen and E. L. Örmeci, Dynamic pricing and scheduling in a multi-class single-server queueing system, Queueing Systems, 67 (2011), 305-331.  doi: 10.1007/s11134-011-9214-5.  Google Scholar

[9]

E. B. ÇilE. L. Örmeci and F. Karaesmen, Effects of system parameters on the optimal policy structure in a class of queueing control problems, Queueing Systems, 61 (2009), 273-304.  doi: 10.1007/s11134-009-9109-x.  Google Scholar

[10]

S. D. FlapperJ.-P. Gayon and L. L. Lim, On the optimal control of manufacturing and remanufacturing activities with a single shared server, European Journal of Operational Research, 234 (2014), 86-98.  doi: 10.1016/j.ejor.2013.10.049.  Google Scholar

[11]

D. GaverP. Jacobs and G. Latouche, Finite birth-and-death models in randomly changing environments, Advances in Applied Probability, 16 (1984), 715-731.  doi: 10.1017/S0001867800022916.  Google Scholar

[12]

B. Hajek, Optimal control of two interacting service stations, IEEE Transactions on Automatic Control, 29 (1984), 491-499.  doi: 10.1109/TAC.1984.1103577.  Google Scholar

[13]

W. E. Helm and K.-H. Waldmann, Optimal control of arrivals to multiserver queues in a random environment, Journal of Applied Probability, 21 (1984), 602-615.  doi: 10.1017/S0021900200028795.  Google Scholar

[14]

D. P. Heyman, Optimal operating policies for M/G/1 queuing systems, Operations Research, 16 (1968), 362-382.  doi: 10.1287/opre.16.2.362.  Google Scholar

[15]

G. Koole, Monotonicity in Markov Reward and Decision Chains: Theory and Applications vol. 1, Now Publishers Inc, 2007. doi: 10.1561/0900000002.  Google Scholar

[16]

B. K. Kumar and J. Raja, On multiserver feedback retrial queues with balking and control retrial rate, Annals of Operations Research, 141 (2006), 211-232.  doi: 10.1007/s10479-006-5300-1.  Google Scholar

[17]

B. K. KumarR. Rukmani and V. Thangaraj, On multiserver feedback retrial queue with finite buffer, Applied Mathematical Modelling, 33 (2009), 2062-2083.  doi: 10.1016/j.apm.2008.05.011.  Google Scholar

[18] M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, New York, 1994.   Google Scholar
[19]

L. I. Sennott, Stochastic Dynamic Programming and the Control of Queueing Systems vol. 504, John Wiley & Sons, New York, 1999.  Google Scholar

[20]

J.-D. Son, Optimal admission and pricing control problem with deterministic service times and sideline profit, Queueing Systems, 60 (2008), 71-85.  doi: 10.1007/s11134-008-9087-4.  Google Scholar

[21]

S. Stidham Jr and R. Weber, A survey of markov decision models for control of networks of queues, Queueing Systems, 13 (1993), 291-314.  doi: 10.1007/BF01158935.  Google Scholar

[22]

H. C. Tijms, Stochastic Models: An Algorithmic Approach vol. 303, John Wiley & Sons Inc, 1994.  Google Scholar

[23]

T. Van Do, An efficient computation algorithm for a multiserver feedback retrial queue with a large queueing capacity, Applied Mathematical Modelling, 34 (2010), 2272-2278.  doi: 10.1016/j.apm.2009.10.025.  Google Scholar

[24]

J. Wu and Z. Lian, Analysis of the $M_{1}, M_{2}$/G/1 G-queueing system with retrial customers, Nonlinear Analysis: Real World Applications, 14 (2013), 365-382.  doi: 10.1016/j.nonrwa.2012.06.009.  Google Scholar

[25]

J. WuJ. Wang and Z. Liu, A discrete-time Geo/G/1 retrial queue with preferred and impatient customers, Applied Mathematical Modelling, 37 (2013), 2552-2561.  doi: 10.1016/j.apm.2012.06.011.  Google Scholar

[26]

S. Yoon and M. E. Lewis, Optimal pricing and admission control in a queueing system with periodically varying parameters, Queueing Systems, 47 (2004), 177-199.  doi: 10.1023/B:QUES.0000035313.20223.3f.  Google Scholar

[27]

X. ZhangJ. Wang and T. Van Do, Threshold properties of the M/M/1 queue under T-policy with applications, Applied Mathematics and Computation, 261 (2015), 284-301.  doi: 10.1016/j.amc.2015.03.109.  Google Scholar

show all references

References:
[1]

H.-S. AhnI. Duenyas and M. E. Lewis, Optimal control of a two-stage tandem queuing system with flexible servers, Probability in the Engineering and Informational Sciences, 16 (2002), 453-469.  doi: 10.1017/S0269964802164047.  Google Scholar

[2]

A. S. Alfa and K. S. Isotupa, An M/PH/k retrial queue with finite number of sources, Computers & Operations Research, 31 (2004), 1455-1464.  doi: 10.1016/S0305-0548(03)00100-X.  Google Scholar

[3]

J. R. Artalejo, Accessible bibliography on retrial queues: Progress in 2000--2009, Mathematical and Computer Modelling, 51 (2010), 1071-1081.  doi: 10.1016/j.mcm.2009.12.011.  Google Scholar

[4]

Y. Aviv and A. Federgruen, The value iteration method for countable state markov decision processes, Operations Research Letters, 24 (1999), 223-234.  doi: 10.1016/S0167-6377(99)00015-2.  Google Scholar

[5]

S. BenjaafarJ.-P. Gayon and S. Tepe, Optimal control of a production--inventory system with customer impatience, Operations Research Letters, 38 (2010), 267-272.  doi: 10.1016/j.orl.2010.03.008.  Google Scholar

[6]

L. Breuer, Threshold policies for controlled retrial queues with heterogeneous servers, Annals of Operations Research, 141 (2006), 139-162.  doi: 10.1007/s10479-006-5297-5.  Google Scholar

[7]

R. Cavazos-Cadena and L. I. Sennott, Comparing recent assumptions for the existence of average optimal stationary policies, Operations Research Letters, 11 (1992), 33-37.  doi: 10.1016/0167-6377(92)90059-C.  Google Scholar

[8]

E. B. ÇilF. Karaesmen and E. L. Örmeci, Dynamic pricing and scheduling in a multi-class single-server queueing system, Queueing Systems, 67 (2011), 305-331.  doi: 10.1007/s11134-011-9214-5.  Google Scholar

[9]

E. B. ÇilE. L. Örmeci and F. Karaesmen, Effects of system parameters on the optimal policy structure in a class of queueing control problems, Queueing Systems, 61 (2009), 273-304.  doi: 10.1007/s11134-009-9109-x.  Google Scholar

[10]

S. D. FlapperJ.-P. Gayon and L. L. Lim, On the optimal control of manufacturing and remanufacturing activities with a single shared server, European Journal of Operational Research, 234 (2014), 86-98.  doi: 10.1016/j.ejor.2013.10.049.  Google Scholar

[11]

D. GaverP. Jacobs and G. Latouche, Finite birth-and-death models in randomly changing environments, Advances in Applied Probability, 16 (1984), 715-731.  doi: 10.1017/S0001867800022916.  Google Scholar

[12]

B. Hajek, Optimal control of two interacting service stations, IEEE Transactions on Automatic Control, 29 (1984), 491-499.  doi: 10.1109/TAC.1984.1103577.  Google Scholar

[13]

W. E. Helm and K.-H. Waldmann, Optimal control of arrivals to multiserver queues in a random environment, Journal of Applied Probability, 21 (1984), 602-615.  doi: 10.1017/S0021900200028795.  Google Scholar

[14]

D. P. Heyman, Optimal operating policies for M/G/1 queuing systems, Operations Research, 16 (1968), 362-382.  doi: 10.1287/opre.16.2.362.  Google Scholar

[15]

G. Koole, Monotonicity in Markov Reward and Decision Chains: Theory and Applications vol. 1, Now Publishers Inc, 2007. doi: 10.1561/0900000002.  Google Scholar

[16]

B. K. Kumar and J. Raja, On multiserver feedback retrial queues with balking and control retrial rate, Annals of Operations Research, 141 (2006), 211-232.  doi: 10.1007/s10479-006-5300-1.  Google Scholar

[17]

B. K. KumarR. Rukmani and V. Thangaraj, On multiserver feedback retrial queue with finite buffer, Applied Mathematical Modelling, 33 (2009), 2062-2083.  doi: 10.1016/j.apm.2008.05.011.  Google Scholar

[18] M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, New York, 1994.   Google Scholar
[19]

L. I. Sennott, Stochastic Dynamic Programming and the Control of Queueing Systems vol. 504, John Wiley & Sons, New York, 1999.  Google Scholar

[20]

J.-D. Son, Optimal admission and pricing control problem with deterministic service times and sideline profit, Queueing Systems, 60 (2008), 71-85.  doi: 10.1007/s11134-008-9087-4.  Google Scholar

[21]

S. Stidham Jr and R. Weber, A survey of markov decision models for control of networks of queues, Queueing Systems, 13 (1993), 291-314.  doi: 10.1007/BF01158935.  Google Scholar

[22]

H. C. Tijms, Stochastic Models: An Algorithmic Approach vol. 303, John Wiley & Sons Inc, 1994.  Google Scholar

[23]

T. Van Do, An efficient computation algorithm for a multiserver feedback retrial queue with a large queueing capacity, Applied Mathematical Modelling, 34 (2010), 2272-2278.  doi: 10.1016/j.apm.2009.10.025.  Google Scholar

[24]

J. Wu and Z. Lian, Analysis of the $M_{1}, M_{2}$/G/1 G-queueing system with retrial customers, Nonlinear Analysis: Real World Applications, 14 (2013), 365-382.  doi: 10.1016/j.nonrwa.2012.06.009.  Google Scholar

[25]

J. WuJ. Wang and Z. Liu, A discrete-time Geo/G/1 retrial queue with preferred and impatient customers, Applied Mathematical Modelling, 37 (2013), 2552-2561.  doi: 10.1016/j.apm.2012.06.011.  Google Scholar

[26]

S. Yoon and M. E. Lewis, Optimal pricing and admission control in a queueing system with periodically varying parameters, Queueing Systems, 47 (2004), 177-199.  doi: 10.1023/B:QUES.0000035313.20223.3f.  Google Scholar

[27]

X. ZhangJ. Wang and T. Van Do, Threshold properties of the M/M/1 queue under T-policy with applications, Applied Mathematics and Computation, 261 (2015), 284-301.  doi: 10.1016/j.amc.2015.03.109.  Google Scholar

Figure 1.  Optimal thresholds vs. $h$ for $\lambda=0.9, \mu=0.1, \xi=0.8, r=40, c=35$
Figure 2.  Optimal thresholds vs. $r$ for $\lambda=1, \mu=1, \xi=0.6, h=0.6, c=30$
Figure 3.  Optimal thresholds vs. $c$ for $\lambda=1, \mu=1, \xi=0.5, h=0.6, r=40$
Table 1.  Optimal thresholds and average cost vs. $\lambda$ for $\mu=1, \xi=0.6, h=0.8, r=30, c=28$
arrival rate $\lambda$Optimal thresholds and average cost $(m, n, g^{*})$
$K=1$ $K=5$ $K=10$ $K=15$
0.8(4, 3, 8.1688)(17, 14, 0.7604)(22, 18, 0.2148)(22, 18, 0.0699)
0.85(3, 3, 9.3409)(14, 11, 1.2510)(16, 12, 0.4664)(16, 12, 0.2033)
0.9(3, 2, 10.5053)(11, 8, 1.9359)(12, 8, 0.9216)(10, 7, 0.5109)
0.95(3, 2, 11.6892)(9, 6, 2.7854)(8, 5, 1.5937)(7, 4, 1.0490)
1(3, 2, 12.9001)(7, 5, 3.7574)(6, 3, 2.4441)(4, 1, 1.8044)
1.05(3, 2, 14.1340)(6, 4, 4.8209)(4, 2, 3.4291)(2, 1, 2.7468)
arrival rate $\lambda$Optimal thresholds and average cost $(m, n, g^{*})$
$K=1$ $K=5$ $K=10$ $K=15$
0.8(4, 3, 8.1688)(17, 14, 0.7604)(22, 18, 0.2148)(22, 18, 0.0699)
0.85(3, 3, 9.3409)(14, 11, 1.2510)(16, 12, 0.4664)(16, 12, 0.2033)
0.9(3, 2, 10.5053)(11, 8, 1.9359)(12, 8, 0.9216)(10, 7, 0.5109)
0.95(3, 2, 11.6892)(9, 6, 2.7854)(8, 5, 1.5937)(7, 4, 1.0490)
1(3, 2, 12.9001)(7, 5, 3.7574)(6, 3, 2.4441)(4, 1, 1.8044)
1.05(3, 2, 14.1340)(6, 4, 4.8209)(4, 2, 3.4291)(2, 1, 2.7468)
Table 2.  Optimal thresholds and average cost vs. $\mu$ for $\lambda=0.8, \xi=0.6, h=0.8, r=30, c=28$
service rate $\mu$Optimal thresholds and average cost $(m, n, g^{*})$
$K=1$ $K=5$ $K=10$ $K=15$
0.75(3, 2, 10.7715)(5, 3, 4.1044)(3, 1, 2.9138)(2, 1, 2.3607)
0.8(3, 2, 10.1999)(6, 4, 3.1936)(5, 2, 2.0532)(3, 1, 1.4776)
0.85(3, 2, 9.6629)(8, 6, 2.3784)(7, 4, 1.3332)(6, 3, 0.8377)
0.9(3, 2, 9.1583)(11, 8, 1.6889)(11, 8, 0.7815)(10, 6, 0.4095)
0.95(3, 3, 8.6564)(14, 11, 1.1467)(16, 12, 0.4167)(15, 11, 0.1738)
1(4, 3, 8.1688)(17, 14, 0.7604)(22, 18, 0.2148)(22, 18, 0.0699)
service rate $\mu$Optimal thresholds and average cost $(m, n, g^{*})$
$K=1$ $K=5$ $K=10$ $K=15$
0.75(3, 2, 10.7715)(5, 3, 4.1044)(3, 1, 2.9138)(2, 1, 2.3607)
0.8(3, 2, 10.1999)(6, 4, 3.1936)(5, 2, 2.0532)(3, 1, 1.4776)
0.85(3, 2, 9.6629)(8, 6, 2.3784)(7, 4, 1.3332)(6, 3, 0.8377)
0.9(3, 2, 9.1583)(11, 8, 1.6889)(11, 8, 0.7815)(10, 6, 0.4095)
0.95(3, 3, 8.6564)(14, 11, 1.1467)(16, 12, 0.4167)(15, 11, 0.1738)
1(4, 3, 8.1688)(17, 14, 0.7604)(22, 18, 0.2148)(22, 18, 0.0699)
Table 3.  Optimal thresholds and average cost vs. $\xi$ for $\lambda=1, \mu=1, h=0.3, r=35, c=33$
retrial rate $\xi$Optimal thresholds and average cost $(m, n, g^{*})$
$K=1$ $K=5$ $K=10$ $K=15$
0.1(1, 1, 17.0052)(5, 3, 4.9228)(10, 6, 2.4449)(15, 9, 1.6616)
0.12(2, 1, 16.8627)(6, 3, 4.7616)(12, 7, 2.3554)(16, 10, 1.6257)
0.14(2, 1, 16.7071)(6, 4, 4.6076)(13, 8, 2.2816)(17, 10, 1.6030)
0.16(2, 2, 16.5549)(7, 4, 4.4670)(14, 9, 2.2220)(17, 11, 1.5898)
0.18(2, 2, 16.4067)(8, 5, 4.3381)(15, 10, 2.1744)(17, 11, 1.5828)
0.2(2, 2, 16.2641)(8, 5, 4.2173)(15, 10, 2.1358)(18, 11, 1.5809)
retrial rate $\xi$Optimal thresholds and average cost $(m, n, g^{*})$
$K=1$ $K=5$ $K=10$ $K=15$
0.1(1, 1, 17.0052)(5, 3, 4.9228)(10, 6, 2.4449)(15, 9, 1.6616)
0.12(2, 1, 16.8627)(6, 3, 4.7616)(12, 7, 2.3554)(16, 10, 1.6257)
0.14(2, 1, 16.7071)(6, 4, 4.6076)(13, 8, 2.2816)(17, 10, 1.6030)
0.16(2, 2, 16.5549)(7, 4, 4.4670)(14, 9, 2.2220)(17, 11, 1.5898)
0.18(2, 2, 16.4067)(8, 5, 4.3381)(15, 10, 2.1744)(17, 11, 1.5828)
0.2(2, 2, 16.2641)(8, 5, 4.2173)(15, 10, 2.1358)(18, 11, 1.5809)
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