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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. Ahn, I. 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. Benjaafar, J.-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. Çil, F. 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. Çil, E. 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. Flapper, J.-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. Gaver, P. 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. Kumar, R. 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. Wu, J. 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. Zhang, J. 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. Ahn, I. 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. Benjaafar, J.-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. Çil, F. 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. Çil, E. 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. Flapper, J.-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. Gaver, P. 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. Kumar, R. 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. Wu, J. 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. Zhang, J. 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
Optimal thresholds vs. $h$ for $\lambda=0.9, \mu=0.1, \xi=0.8, r=40, c=35$
Optimal thresholds vs. $r$ for $\lambda=1, \mu=1, \xi=0.6, h=0.6, c=30$
Optimal thresholds vs. $c$ for $\lambda=1, \mu=1, \xi=0.5, h=0.6, r=40$
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)
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)
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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