American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Optimum pricing strategy for complementary products with reservation price in a supply chain model
  • JIMO Home
  • This Issue
  • Next Article
    Stochastic machine breakdown and discrete delivery in an imperfect inventory-production system
July  2017, 13(3): 1537-1552. doi: 10.3934/jimo.2017006

Optimal threshold control of a retrial queueing system with finite buffer

Gang Chen , Zaiming Liu and  Jinbiao Wu ,

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.

Keywords: Retrial queueing system, Markov decision process, threshold control, matrix analytic method, performance evaluation.
Mathematics Subject Classification: Primary: 60K25; Secondary: 90B22.
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

Figure 1.  Optimal thresholds vs. $h$ for $\lambda=0.9, \mu=0.1, \xi=0.8, r=40, c=35$
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Optimal thresholds vs. $r$ for $\lambda=1, \mu=1, \xi=0.6, h=0.6, c=30$
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Optimal thresholds vs. $c$ for $\lambda=1, \mu=1, \xi=0.5, h=0.6, r=40$
Figure Options

Download full-size image

Download as PowerPoint slide

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)
Table Options

Download as excel

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)
Table Options

Download as excel

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)
Table Options

Download as excel

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)
[1]

Tuan Phung-Duc, Wouter Rogiest, Sabine Wittevrongel. Single server retrial queues with speed scaling: Analysis and performance evaluation. Journal of Industrial & Management Optimization, 2017, 13 (4) : 1927-1943. doi: 10.3934/jimo.2017025
[2]

Vladimir Turetsky, Valery Y. Glizer. Optimal decision in a Statistical Process Control with cubic loss. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021096
[3]

Zhanyou Ma, Wuyi Yue, Xiaoli Su. Performance analysis of a Geom/Geom/1 queueing system with variable input probability. Journal of Industrial & Management Optimization, 2011, 7 (3) : 641-653. doi: 10.3934/jimo.2011.7.641
[4]

Zhanqiang Huo, Wuyi Yue, Naishuo Tian, Shunfu Jin. Performance evaluation for the sleep mode in the IEEE 802.16e based on a queueing model with close-down time and multiple vacations. Journal of Industrial & Management Optimization, 2009, 5 (3) : 511-524. doi: 10.3934/jimo.2009.5.511
[5]

Madhu Jain, Sudeep Singh Sanga. Admission control for finite capacity queueing model with general retrial times and state-dependent rates. Journal of Industrial & Management Optimization, 2020, 16 (6) : 2625-2649. doi: 10.3934/jimo.2019073
[6]

Wendai Lv, Siping Ji. Atmospheric environmental quality assessment method based on analytic hierarchy process. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 941-955. doi: 10.3934/dcdss.2019063
[7]

Yi Peng, Jinbiao Wu. On the $ BMAP_1, BMAP_2/PH/g, c $ retrial queueing system. Journal of Industrial & Management Optimization, 2021, 17 (6) : 3373-3391. doi: 10.3934/jimo.2020124
[8]

Sin-Man Choi, Ximin Huang, Wai-Ki Ching. Minimizing equilibrium expected sojourn time via performance-based mixed threshold demand allocation in a multiple-server queueing environment. Journal of Industrial & Management Optimization, 2012, 8 (2) : 299-323. doi: 10.3934/jimo.2012.8.299
[9]

Yuan Zhao, Wuyi Yue. Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users. Journal of Industrial & Management Optimization, 2019, 15 (1) : 1-14. doi: 10.3934/jimo.2018029
[10]

Omer Faruk Yilmaz, Mehmet Bulent Durmusoglu. A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment. Journal of Industrial & Management Optimization, 2018, 14 (3) : 1219-1249. doi: 10.3934/jimo.2018007
[11]

Zhanyou Ma, Wenbo Wang, Linmin Hu. Performance evaluation and analysis of a discrete queue system with multiple working vacations and non-preemptive priority. Journal of Industrial & Management Optimization, 2020, 16 (3) : 1135-1148. doi: 10.3934/jimo.2018196
[12]

Dequan Yue, Wuyi Yue. Block-partitioning matrix solution of M/M/R/N queueing system with balking, reneging and server breakdowns. Journal of Industrial & Management Optimization, 2009, 5 (3) : 417-430. doi: 10.3934/jimo.2009.5.417
[13]

Mathias Staudigl. A limit theorem for Markov decision processes. Journal of Dynamics & Games, 2014, 1 (4) : 639-659. doi: 10.3934/jdg.2014.1.639
[14]

Zhanyou Ma, Pengcheng Wang, Wuyi Yue. Performance analysis and optimization of a pseudo-fault Geo/Geo/1 repairable queueing system with N-policy, setup time and multiple working vacations. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1467-1481. doi: 10.3934/jimo.2017002
[15]

Sung-Seok Ko. A nonhomogeneous quasi-birth-death process approach for an $ (s, S) $ policy for a perishable inventory system with retrial demands. Journal of Industrial & Management Optimization, 2020, 16 (3) : 1415-1433. doi: 10.3934/jimo.2019009
[16]

Bara Kim. Stability of a retrial queueing network with different classes of customers and restricted resource pooling. Journal of Industrial & Management Optimization, 2011, 7 (3) : 753-765. doi: 10.3934/jimo.2011.7.753
[17]

Ning Chen, Yan Xia Zhao, Jia Yang Dai, Yu Qian Guo, Wei Hua Gui, Jun Jie Peng. Hybrid modeling and distributed optimization control method for the iron removal process. Journal of Industrial & Management Optimization, 2022  doi: 10.3934/jimo.2022003
[18]

Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad. Optimizing multi-objective decision making having qualitative evaluation. Journal of Industrial & Management Optimization, 2015, 11 (3) : 747-762. doi: 10.3934/jimo.2015.11.747
[19]

Shunfu Jin, Wuyi Yue, Zhanqiang Huo. Performance evaluation for connection oriented service in the next generation Internet. Numerical Algebra, Control & Optimization, 2011, 1 (4) : 749-761. doi: 10.3934/naco.2011.1.749
[20]

Shunfu Jin, Wuyi Yue, Chao Meng, Zsolt Saffer. A novel active DRX mechanism in LTE technology and its performance evaluation. Journal of Industrial & Management Optimization, 2015, 11 (3) : 849-866. doi: 10.3934/jimo.2015.11.849

2020 Impact Factor: 1.801

Tools

Metrics

  • PDF downloads (187)
  • HTML views (409)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy