The residual waiting time process, also known as the residual life process, represents the remaining time until the next renewal event, observed at an arbitrary moment. This process arises naturally in diverse areas such as queueing systems, reliability analysis, and inventory modeling. However, obtaining exact expressions for the expected residual waiting time is often analytically challenging, especially when the interarrival time distribution deviates from the Erlang distribution case. In this study, we propose intuitive approximations for the expected value of residual waiting time process, based on intuitive approximation of the renewal function. Two classes of interarrival distributions are examined: heavy-tailed distributions with regularly varying tails, and light-tailed distributions belonging to the special class of distributions denoted by $ \Gamma(g) $, which naturally arises in extreme value theory. Using theoretical results from renewal theory and equilibrium distributions, intuitive approximation formulas are derived for both distributional settings. In particular, we investigate the Erlang distribution as a case study, comparing expected value of the residual waiting time computed via the exact renewal function with that obtained from the intuitive approximation. Moreover, for the Pareto and Burr XII distributions, we conduct case studies demonstrating how intuitive approximation closely matches asymptotic results for the expected value of the residual waiting time in the absence of exact formulas. This work provides a practical and mathematically grounded framework for analyzing systems involving stochastic arrivals, with potential extensions to higher-order moments.
Citation: Tülay Yazır, Aslı Bektaş Kamışlık, Tahir Khaniyev. Intuitive approximations for a residual waiting time process[J]. AIMS Mathematics, 2025, 10(12): 28629-28650. doi: 10.3934/math.20251260
The residual waiting time process, also known as the residual life process, represents the remaining time until the next renewal event, observed at an arbitrary moment. This process arises naturally in diverse areas such as queueing systems, reliability analysis, and inventory modeling. However, obtaining exact expressions for the expected residual waiting time is often analytically challenging, especially when the interarrival time distribution deviates from the Erlang distribution case. In this study, we propose intuitive approximations for the expected value of residual waiting time process, based on intuitive approximation of the renewal function. Two classes of interarrival distributions are examined: heavy-tailed distributions with regularly varying tails, and light-tailed distributions belonging to the special class of distributions denoted by $ \Gamma(g) $, which naturally arises in extreme value theory. Using theoretical results from renewal theory and equilibrium distributions, intuitive approximation formulas are derived for both distributional settings. In particular, we investigate the Erlang distribution as a case study, comparing expected value of the residual waiting time computed via the exact renewal function with that obtained from the intuitive approximation. Moreover, for the Pareto and Burr XII distributions, we conduct case studies demonstrating how intuitive approximation closely matches asymptotic results for the expected value of the residual waiting time in the absence of exact formulas. This work provides a practical and mathematically grounded framework for analyzing systems involving stochastic arrivals, with potential extensions to higher-order moments.
| [1] | D. R. Cox, Renewal Theory, London: Methuen & Co., 1967. |
| [2] | E. A. Elsayed, Reliability Engineering, New York: Prentice Hall, 1996. |
| [3] | A. A. Borovkov, Stochastic Processes in Queuing Theory, New York: Springer-Verlag, 1976. |
| [4] |
R. Aliyev, Ö. Ardıç, T. Khaniyev, Asymptotic approach for a renewal-reward process with a general interference of chance, Commun. Stat. Theory Meth., 45 (2016), 4237–4248. https://doi.org/10.1080/03610926.2014.917679 doi: 10.1080/03610926.2014.917679
|
| [5] | M. Brown, H. A. Solomon, Second-order approximation for the variance of a renewal-reward process, Stoch. Process. Appl., 3 (1975), 301–314. |
| [6] |
A. Csenki, Asymptotics for renewal-reward processes with retrospective reward structure, Oper. Res. Lett., 26 (2000), 201–209. https://doi.org/10.1016/S0167-6377(00)00035-3 doi: 10.1016/S0167-6377(00)00035-3
|
| [7] | Z. Hanalioglu, T. Khaniyev, Asymptotic results for an inventory model of type $(s, S)$ with asymmetric triangular distributed interference of chance and delay, GU J. Sci., 31 (2018), 174–187. |
| [8] | A. B. Kamışlık, T. Kesemen, T. Khaniyev, Inventory model of type $(s, S)$ under heavy-tailed demand with infinite variance, Braz. J. Probab. Stat., 33 (2019), 39–56. |
| [9] |
A. B. Kamışlık, B. Alakoç, T. Kesemen, T. Khaniyev, A semi-Markovian renewal reward process with $\Gamma(g)$ distributed demand, Turk. J. Math., 44 (2020), 1250–1262. https://doi.org/10.3906/mat-2002-72 doi: 10.3906/mat-2002-72
|
| [10] |
A. B. Kamışlık, F. Baghezze, T. Kesemen, T. Khaniyev, Moment-based approximations for stochastic control model of type $(s, S)$, Commun. Stat. Theory Meth., 53 (2024), 7505–7516. https://doi.org/10.1080/03610926.2023.2268765 doi: 10.1080/03610926.2023.2268765
|
| [11] | T. A. Khaniyev, About moments of generalized renewal process, Trans. NAS Azerb., Ser. Phys. Tech. Math. Sci., 25 (2005), 95–100. |
| [12] | T. Khaniyev, K. D. Atalay, On the weak convergence of the ergodic distribution for an inventory model of type $(s, S)$, Hacet. J. Math. Stat., 39 (2010), 599–611. |
| [13] | T. Khaniyev, C. Aksop, Asymptotic results for an inventory model of type $(s, S)$ with a generalized beta interference of chance, TWMS J. Appl. Eng. Math., 2 (2018), 223–236. |
| [14] |
T. Khaniyev, A. Kokangül, R. T. Aliyev, An asymptotic approach for a semi-Markovian inventory model of type $(s, S)$, Appl. Stoch. Models Bus. Ind., 29 (2013), 439–453. https://doi.org/10.1002/asmb.1918 doi: 10.1002/asmb.1918
|
| [15] |
T. Yazır, A. B. Kamışlık, T. Khaniyev, Z. Hanalioglu, Moment-based approximation for a renewal reward process with generalized gamma-distributed interference of chance, Demonstr. Math., 58 (2025), 20250153. https://doi.org/10.1515/dema-2025-0153 doi: 10.1515/dema-2025-0153
|
| [16] | W. Feller, An Introduction to Probability Theory and Its Applications, 2 Eds., New York: John Wiley & Sons, 1971. |
| [17] | S. M. Ross, Stochastic Processes, 2 Eds., New York: John Wiley & Sons, 1996. |
| [18] | H. M. Taylor, S. Karlin, An Introduction to Stochastic Modeling, 3 Eds., San Diego: Gulf Professional Publishing, 1998. |
| [19] | J. Medhi, Stochastic Processes, 3 Eds., New Delhi: New Academic Science (New Age Int.), 2009. |
| [20] | S. Asmussen, Applied Probability and Queues, 2 Eds., New York: Springer, 2003. |
| [21] |
D. M. Bradley, R. C. Gupta, Limiting behaviour of the mean residual life, Ann. Inst. Stat. Math., 55 (2003), 217–226. https://doi.org/10.1007/BF02530495 doi: 10.1007/BF02530495
|
| [22] |
K. Lange, N. Risch, C. Louy, Laplace transforms for residual waiting times, Appl. Math. Comput., 3 (1977), 253–264. https://doi.org/10.1016/0096-3003(77)90005-4 doi: 10.1016/0096-3003(77)90005-4
|
| [23] |
M. Z. Raqaba, M. Asadi, Some results on the mean residual waiting time of records, Statistics, 44 (2010), 493–504. https://doi.org/10.1080/02331880903189158 doi: 10.1080/02331880903189158
|
| [24] |
A. K. Nanda, S. Bhattacharjee, N. Balakrishnan, Mean residual life function, associated orderings and properties, IEEE Trans. Reliab., 59 (2010), 55–65. https://doi.org/10.1109/TR.2009.2035791 doi: 10.1109/TR.2009.2035791
|
| [25] |
R. Ahmadi, Z. Rasaei, R. Farnoosh, An approach to modeling residual life of a renewal process for reliability analysis and maintenance planning, Comput. Ind. Eng., 183 (2023), 109510. https://doi.org/10.1016/j.cie.2023.109510 doi: 10.1016/j.cie.2023.109510
|
| [26] |
G. Morvai, B. Weiss, Universal rates for estimating the residual waiting time in an intermittent way, Kybernetika, 56 (2020), 601–616. http://doi.org/10.14736/kyb-2020-4-0601 doi: 10.14736/kyb-2020-4-0601
|
| [27] |
M. Haviva, J. van der Wal, Waiting times in queues with relative priorities, Oper. Res. Lett., 35 (2007), 591–594. https://doi.org/10.1016/j.orl.2006.10.003 doi: 10.1016/j.orl.2006.10.003
|
| [28] |
B. Van Houdt, C. Blondia, Approximated transient queue length and waiting time distributions via steady state analysis, Stoch. Models, 21 (2007), 725–744. https://doi.org/10.1081/STM-200056027 doi: 10.1081/STM-200056027
|
| [29] |
D. L. Jagerman, B. Balcıoğlu, T. Altıok, B. Melmed, Mean waiting time approximations in the G/G/1 queue, Queueing Syst., 46 (2004), 481–506. https://doi.org/10.1023/B:QUES.0000027996.28824.89 doi: 10.1023/B:QUES.0000027996.28824.89
|
| [30] | R. W. Wolff, Stochastic Modeling and the Theory of Queues, Englewood Cliffs: Prentice Hall, 1989. |
| [31] | R. G. Gallager, Stochastic Processes: Theory for Applications, Cambridge: Cambridge University Press, 2013. |
| [32] |
B. A. Rogozin, On the distribution of the first jump, Theory Probab. Appl., 9 (1964), 450–465. https://doi.org/10.1137/1109060 doi: 10.1137/1109060
|
| [33] |
K. V. Mitov, E. Omey, Intuitive approximations for the renewal function, Stat. Probab. Lett., 84 (2014), 72–80. https://doi.org/10.1016/j.spl.2013.09.030 doi: 10.1016/j.spl.2013.09.030
|
| [34] | N. H. Bingham, C. M. Goldie, J. L. Teugels, Regular Variation, Cambridge: Cambridge University Press, 1987. |
| [35] |
N. H. Bingham, C. M. Goldie, E. Omey, Regularly varying probability densities, Publ. Inst. Math., 80 (2006), 47–57. https://doi.org/10.2298/PIM0694047B doi: 10.2298/PIM0694047B
|
| [36] | S. I. Resnick, Heavy-Tail Phenomena: Probabilistic and Statistical Modeling, New York: Springer, 2006. |
| [37] | E. Seneta, Functions of Regular Variation, New York: Springer, 1976. |
| [38] | M. Abramowitz, I. A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Washington: National Bureau of Standards, 1964. |