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January  2021, 17(1): 299-316. doi: 10.3934/jimo.2019112

Optimal customer behavior in observable and unobservable discrete-time queues

Veena Goswami 1, and  Gopinath Panda 2,

1. 

School of Computer Applications, Kalinga Institute of Industrial Technology, Bhubaneswar-751024, India
2. 

Engineering Systems and Design, Singapore University of Technology and Design, 8 Somapah Rd, Singapore 487372

Received  December 2018 Revised  April 2019 Published  January 2021 Early access  September 2019

This paper studies the effect of information suppression on Naor's model as well as on Edelson and Hildebrand's model under geometric distribution. We set the suitable non-cooperative games and search for their Nash equilibria under the observable and unobservable system. In each case, we analyze the effects of information level on the customers' equilibrium and socially optimal balking strategies as well as on the profit maximization of the system manager. The socially optimal behavior and the inefficiency of the equilibrium strategies are quantified via the price of anarchy measure. We discuss a comparison study of the profit maximization and social welfare under an imposed admission fee. Also, the impact of information on the selfish and social optimal joining rates is examined. Numerical results are presented to exemplify the impact of system parameters on the optimal behavior of customers under different information levels.

Keywords: Discrete-time queue, equilibrium behavior, strategic customers, profit maximization, social optimization, price of anarchy.
Mathematics Subject Classification: Primary: 60K25, 68M20, 90B22; Secondary: 91A10, 91A80.
Citation: Veena Goswami, Gopinath Panda. Optimal customer behavior in observable and unobservable discrete-time queues. Journal of Industrial and Management Optimization, 2021, 17 (1) : 299-316. doi: 10.3934/jimo.2019112
References:
[1]

Y. Bixuan, H. Zhenting, W. Jinbiao and L. Zaiming, Analysis of the equilibrium strategies in the Geo/Geo/1 queue with multiple working vacations, Quality Technology & Quantitative Management, 15 (2018), 663-685.  doi: 10.1080/16843703.2017.1335488.

[2]

O. Boudali and A. Economou, Optimal and equilibrium balking strategies in the single server Markovian queue with catastrophes, European Journal of Operational Research, 218 (2012), 708-715.  doi: 10.1016/j.ejor.2011.11.043.

[3]

A. Burnetas and A. Economou, Equilibrium customer strategies in a single server Markovian queue with setup times, Queueing Systems, 56 (2007), 213-228.  doi: 10.1007/s11134-007-9036-7.

[4]

H. Chen and M. Frank, Monopoly pricing when customers queue, IIE Transactions, 36 (2004), 569-581.  doi: 10.1080/07408170490438690.

[5]

Y. Dimitrakopoulos and A. N. Burnetas, Customer equilibrium and optimal strategies in an M/M/1 queue with dynamic service control, European Journal of Operational Research, 252 (2016), 477-486.  doi: 10.1016/j.ejor.2015.12.029.

[6]

N. M. Edelson and D. K. Hilderbrand, Congestion tolls for poisson queuing processes, Econometrica, 43 (1975), 81-92.  doi: 10.2307/1913415.

[7]

S. Gao and J. T. Wang, Equilibrium balking strategies in the observable Geo/Geo/1 queue with delayed multiple vacations, RAIRO-Operations Research, 50 (2016), 119-129.  doi: 10.1051/ro/2015019.

[8]

G. Gilboa-Freedman, R. Hassin and Y. Kerner, The price of anarchy in the Markovian single server queue, IEEE Transactions on Automatic Control, 59 (2014), 455-459.  doi: 10.1109/TAC.2013.2270872.

[9]

V. Goswami and G. Panda, Mixed equilibrium and social joining strategies in Markovian queues with Bernoulli-schedule-controlled vacation and vacation interruption, Quality Technology & Quantitative Management, (2018), 531–559. doi: 10.1080/16843703.2018.1480266.

[10]

V. Goswami and G. Panda, Optimal information policy in discrete-time queues with strategic customers, Journal of Industrial & Management Optimization, 15 (2019), 689-703. 

[11]

P. F. Guo and R. Hassin, Strategic behavior and social optimization in Markovian vacation queues, Operations Research, 59 (2011), 986-997.  doi: 10.1287/opre.1100.0907.

[12]

R. Hassin, Consumer information in markets with random product quality: The case of queues and balking, Econometrica, 54 (1986), 1185-1195.  doi: 10.2307/1912327.

[13] R. Hassin, Rational Queueing, CRC Press, Boca Raton, FL, 2016.  doi: 10.1201/b20014.
[14]

R. Hassin and M. Haviv, To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems, International Series in Operations Research & Management Science, 59. Kluwer Academic Publishers, Boston, MA, 2003. doi: 10.1007/978-1-4615-0359-0.

[15] J. J. Hunter, Mathematical Techniques of Applied Probability: Discrete Time Models: Basic Theory, Vol. 1. Operations Research and Industrial Engineering, Academic Press, Inc., New York, 1983. 
[16]

R. Ibrahim, Sharing delay information in service systems: A literature survey, Queueing Systems, 89 (2018), 49-79.  doi: 10.1007/s11134-018-9577-y.

[17]

L. Li, J. T. Wang and F. Zhang, Equilibrium customer strategies in Markovian queues with partial breakdowns, Computers & Industrial Engineering, 66 (2013), 751-757.  doi: 10.1016/j.cie.2013.09.023.

[18]

Y. Ma, W.-Q. Liu and J.-H. Li, Equilibrium balking behavior in the Geo/Geo/1 queueing system with multiple vacations, Applied Mathematical Modelling, 37 (2013), 3861-3878.  doi: 10.1016/j.apm.2012.08.017.

[19]

P. Naor, The regulation of queue size by levying tolls, Econometrica, 37 (1969), 15-24.  doi: 10.2307/1909200.

[20]

G. Panda and V. Goswami, Effect of information on the strategic behavior of customers in a discrete-time bulk service queue, Journal of Industrial & Management Optimization, 708–715. doi: 10.3934/jimo.2019007.

[21]

G. Panda, V. Goswami and A. D. Banik, Equilibrium and socially optimal balking strategies in Markovian queues with vacations and sequential abandonment, Asia-Pacific Journal of Operational Research, 33 (2016), 1650036, 34 pp. doi: 10.1142/S0217595916500366.

[22]

G. Panda, V. Goswami and A. D. Banik, Equilibrium behaviour and social optimization in Markovian queues with impatient customers and variant of working vacations, RAIRO-Operations Research, 51 (2017), 685-707.  doi: 10.1051/ro/2016056.

[23]

R. Shone, V. A. Knight and J. E. Williams, Comparisons between observable and unobservable M/M/1 queues with respect to optimal customer behavior, European Journal of Operational Research, 227 (2013), 133-141.  doi: 10.1016/j.ejor.2012.12.016.

[24]

W. Sun, S. Y. Li and E. Cheng-Guo, Equilibrium and optimal balking strategies of customers in Markovian queues with multiple vacations and $N$-policy, Applied Mathematical Modelling, 40 (2016), 284-301.  doi: 10.1016/j.apm.2015.04.045.

[25]

T. T. Yang, J. T. Wang and F. Zhang, Equilibrium balking strategies in the Geo/Geo/1 queues with server breakdowns and repairs, Quality Technology & Quantitative Management, 11 (2014), 231-243.  doi: 10.1080/16843703.2014.11673341.

[26]

M. M. Yu and A. S. Alfa, Strategic queueing behavior for individual and social optimization in managing discrete time working vacation queue with Bernoulli interruption schedule, Computers & Operations Research, 73 (2016), 43-55.  doi: 10.1016/j.cor.2016.03.011.

show all references

References:
[1]

Y. Bixuan, H. Zhenting, W. Jinbiao and L. Zaiming, Analysis of the equilibrium strategies in the Geo/Geo/1 queue with multiple working vacations, Quality Technology & Quantitative Management, 15 (2018), 663-685.  doi: 10.1080/16843703.2017.1335488.

[2]

O. Boudali and A. Economou, Optimal and equilibrium balking strategies in the single server Markovian queue with catastrophes, European Journal of Operational Research, 218 (2012), 708-715.  doi: 10.1016/j.ejor.2011.11.043.

[3]

A. Burnetas and A. Economou, Equilibrium customer strategies in a single server Markovian queue with setup times, Queueing Systems, 56 (2007), 213-228.  doi: 10.1007/s11134-007-9036-7.

[4]

H. Chen and M. Frank, Monopoly pricing when customers queue, IIE Transactions, 36 (2004), 569-581.  doi: 10.1080/07408170490438690.

[5]

Y. Dimitrakopoulos and A. N. Burnetas, Customer equilibrium and optimal strategies in an M/M/1 queue with dynamic service control, European Journal of Operational Research, 252 (2016), 477-486.  doi: 10.1016/j.ejor.2015.12.029.

[6]

N. M. Edelson and D. K. Hilderbrand, Congestion tolls for poisson queuing processes, Econometrica, 43 (1975), 81-92.  doi: 10.2307/1913415.

[7]

S. Gao and J. T. Wang, Equilibrium balking strategies in the observable Geo/Geo/1 queue with delayed multiple vacations, RAIRO-Operations Research, 50 (2016), 119-129.  doi: 10.1051/ro/2015019.

[8]

G. Gilboa-Freedman, R. Hassin and Y. Kerner, The price of anarchy in the Markovian single server queue, IEEE Transactions on Automatic Control, 59 (2014), 455-459.  doi: 10.1109/TAC.2013.2270872.

[9]

V. Goswami and G. Panda, Mixed equilibrium and social joining strategies in Markovian queues with Bernoulli-schedule-controlled vacation and vacation interruption, Quality Technology & Quantitative Management, (2018), 531–559. doi: 10.1080/16843703.2018.1480266.

[10]

V. Goswami and G. Panda, Optimal information policy in discrete-time queues with strategic customers, Journal of Industrial & Management Optimization, 15 (2019), 689-703. 

[11]

P. F. Guo and R. Hassin, Strategic behavior and social optimization in Markovian vacation queues, Operations Research, 59 (2011), 986-997.  doi: 10.1287/opre.1100.0907.

[12]

R. Hassin, Consumer information in markets with random product quality: The case of queues and balking, Econometrica, 54 (1986), 1185-1195.  doi: 10.2307/1912327.

[13] R. Hassin, Rational Queueing, CRC Press, Boca Raton, FL, 2016.  doi: 10.1201/b20014.
[14]

R. Hassin and M. Haviv, To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems, International Series in Operations Research & Management Science, 59. Kluwer Academic Publishers, Boston, MA, 2003. doi: 10.1007/978-1-4615-0359-0.

[15] J. J. Hunter, Mathematical Techniques of Applied Probability: Discrete Time Models: Basic Theory, Vol. 1. Operations Research and Industrial Engineering, Academic Press, Inc., New York, 1983. 
[16]

R. Ibrahim, Sharing delay information in service systems: A literature survey, Queueing Systems, 89 (2018), 49-79.  doi: 10.1007/s11134-018-9577-y.

[17]

L. Li, J. T. Wang and F. Zhang, Equilibrium customer strategies in Markovian queues with partial breakdowns, Computers & Industrial Engineering, 66 (2013), 751-757.  doi: 10.1016/j.cie.2013.09.023.

[18]

Y. Ma, W.-Q. Liu and J.-H. Li, Equilibrium balking behavior in the Geo/Geo/1 queueing system with multiple vacations, Applied Mathematical Modelling, 37 (2013), 3861-3878.  doi: 10.1016/j.apm.2012.08.017.

[19]

P. Naor, The regulation of queue size by levying tolls, Econometrica, 37 (1969), 15-24.  doi: 10.2307/1909200.

[20]

G. Panda and V. Goswami, Effect of information on the strategic behavior of customers in a discrete-time bulk service queue, Journal of Industrial & Management Optimization, 708–715. doi: 10.3934/jimo.2019007.

[21]

G. Panda, V. Goswami and A. D. Banik, Equilibrium and socially optimal balking strategies in Markovian queues with vacations and sequential abandonment, Asia-Pacific Journal of Operational Research, 33 (2016), 1650036, 34 pp. doi: 10.1142/S0217595916500366.

[22]

G. Panda, V. Goswami and A. D. Banik, Equilibrium behaviour and social optimization in Markovian queues with impatient customers and variant of working vacations, RAIRO-Operations Research, 51 (2017), 685-707.  doi: 10.1051/ro/2016056.

[23]

R. Shone, V. A. Knight and J. E. Williams, Comparisons between observable and unobservable M/M/1 queues with respect to optimal customer behavior, European Journal of Operational Research, 227 (2013), 133-141.  doi: 10.1016/j.ejor.2012.12.016.

[24]

W. Sun, S. Y. Li and E. Cheng-Guo, Equilibrium and optimal balking strategies of customers in Markovian queues with multiple vacations and $N$-policy, Applied Mathematical Modelling, 40 (2016), 284-301.  doi: 10.1016/j.apm.2015.04.045.

[25]

T. T. Yang, J. T. Wang and F. Zhang, Equilibrium balking strategies in the Geo/Geo/1 queues with server breakdowns and repairs, Quality Technology & Quantitative Management, 11 (2014), 231-243.  doi: 10.1080/16843703.2014.11673341.

[26]

M. M. Yu and A. S. Alfa, Strategic queueing behavior for individual and social optimization in managing discrete time working vacation queue with Bernoulli interruption schedule, Computers & Operations Research, 73 (2016), 43-55.  doi: 10.1016/j.cor.2016.03.011.

Figure 1.  Various time epochs in late-arrival system with delayed access (LAS-DA)
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Figure 2.  State transition diagram of the Geo/Geo/1/$ n_e $ queueing model
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Figure 3.  State transition diagram of the Geo/Geo/1 queue with joining probability $ f $
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Figure 4.  Dependence of threshold strategies on $ R/C $ for $ \lambda = 0.2, \mu = 0.5 $
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Figure 5.  Dependence of threshold strategies on $ \mu $ for $ \lambda = 0.2, R = 30, C = 1 $
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Figure 6.  Dependence of threshold strategies on $ \lambda $ for $ R = 30, \mu = 0.5, C = 1 $
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Figure 7.  PoA vs $ \lambda $ in the observable queue with parameters $ R = 30, C = 1, \mu = 0.5 $
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Figure 8.  PoA vs $ \mu $ in the observable queue with parameters $ \lambda = 0.5, R = 30, C = 1 $
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Figure 9.  $ R/C $ vs mixed strategies for the unobservable case with $ \lambda = 0.2, \mu = 0.5 $
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Figure 10.  $ \mu $ vs mixed strategies for the unobservable case with $ \lambda = 0.5, R = 30, C = 1 $
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Figure 11.  $ \lambda $ vs mixed strategies for the unobservable case with $ R = 10, \mu = 0.6, C = 5 $
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Figure 12.  $ \lambda $ vs socially equilibrium benefit for the observable case with $ R = 30, C = 1, \mu = 0.5 $
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Figure 13.  Social welfare under a profit maximizing fee for the observable case with $ R = 30, C = 1, \mu = 0.5 $
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Figure 14.  Selfish optimal joining rate comparison
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Figure 15.  Socially optimal joining rate comparison
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Table 1.  Equilibrium joining strategy
Case $ f_e $ $ \lambda_e $ $ W_e $
$ \lambda\le \mu-\frac{C\bar{\mu}}{R-C} $ $ 1 $ $ \lambda $ $ \frac{\bar{\lambda}}{\mu-\lambda} $
$ 0\le \mu-\frac{C\bar{\mu}}{R-C} < \lambda $ $ \frac{1}{\lambda}(\mu-\frac{C\bar{\mu}}{R-C}) $ $ \mu-\frac{C\bar{\mu}}{R-C} $ $ \frac{R}{C} $
$ \mu-\frac{C\bar{\mu}}{R-C} <0 $ 0 0 $ \frac{1}{\mu} $
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Case $ f_e $ $ \lambda_e $ $ W_e $
$ \lambda\le \mu-\frac{C\bar{\mu}}{R-C} $ $ 1 $ $ \lambda $ $ \frac{\bar{\lambda}}{\mu-\lambda} $
$ 0\le \mu-\frac{C\bar{\mu}}{R-C} < \lambda $ $ \frac{1}{\lambda}(\mu-\frac{C\bar{\mu}}{R-C}) $ $ \mu-\frac{C\bar{\mu}}{R-C} $ $ \frac{R}{C} $
$ \mu-\frac{C\bar{\mu}}{R-C} <0 $ 0 0 $ \frac{1}{\mu} $
Table 2.  Socially optimal joining strategy
Case $ f_s $ $ \lambda_s $ $ W_s $
$ \lambda\le \mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}} $ $ 1 $ $ \lambda $ $ \frac{\bar{\lambda}}{\mu-\lambda} $
$ \lambda > \mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}} $ $ \frac{\mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}}}{\lambda} $ $ \mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}} $ $ 1+\bar{\mu}\sqrt{\frac{R-C}{C\mu\bar{\mu}}} $
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Case $ f_s $ $ \lambda_s $ $ W_s $
$ \lambda\le \mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}} $ $ 1 $ $ \lambda $ $ \frac{\bar{\lambda}}{\mu-\lambda} $
$ \lambda > \mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}} $ $ \frac{\mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}}}{\lambda} $ $ \mu-\sqrt{\frac{C\mu\bar{\mu}}{R-C}} $ $ 1+\bar{\mu}\sqrt{\frac{R-C}{C\mu\bar{\mu}}} $
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