A New M/M/1 Queueing System with Bridging Gap Minimization
Keywords:
Queueing theory , Kendall notation , Exponential distribution , Bridging function , Moment generating function , PSO algorithmAbstract
In queueing systems, the Kendall notation a/b/x/q/y/z is a standard format used to describe six categories: the arrival distribution of clients, the service distribution of servers, servers’ number, the queue capacity, the system capacity, and the queue discipline. In this paper, a new category, referred to as the bridging function with best alpha (Br_∝*) between the arrival and service distributions, is introduced into the Kendall notation. It is placed between the arrival_distribution and the service_distribution, replacing the queue capacity (b), so that the notation becomes (a/ Br_∝*/b/x/y/z). Based on this new notation, the capacity rate of the queueing line can be predicted and represents the gap between the client arrival rate and the client service rate. It is shown that the proposed bridging function with best alpha follows the same distribution of arrival and service, both of which are assumed to be exponentially distributed. The Particle Swarm Optimization (PSO) algorithm is employed to find the optimal rate of best alpha, using the bridging function for the arrival and service distributions as the objective function. Simulation results of M/ Br_∝*/M/1 suggest that the bridging function with best alpha (Br_∝*) can reduce the delay time in the queueing system and converge to the optimal solution when applied to a standard M/M/1 system simulated by MATLAB code.
References
[1] Yang, X.S.; "Optimization Techniques and Applications with Examples". 1st ed.; Wiley Blackwell: Hoboken, NJ, USA, 2018.
[2] Kobayashi, H.; Turin, W.; Mark, B.L.; "Probability, Random Processes, and Statistical Analysis". 1st ed.; Cambridge University Press: New York, NY, USA, 2012.
[3] Abo-Alsabeh R., Daham H. A.; Salhi A.; "An evolutionary approach for solving the minimum volume ellipsoid estimator problem". In: Innovations in bio-inspired computing and applications; IBICA 2020, Abraham, A.; Sasaki, H.; Rios, R.; et al. (eds); Advances in Intelligent Systems and Computing, Springer: Cham, Switzerland, pp. 23–31, 2021
[4] Narayan, B.U.; "An Introduction to Queueing Theory: Modeling and Analysis in Applications". 1st ed.; Birkhäuser: Boston, MA, USA, 2015.
[5] Smith, J.M.; "Introduction to Queueing Networks: Theory Practice". 1st ed.; Springer: Berlin, Germany, 2019.
[6] Salam, N.A.; Farah, F.L.; Abdalmaaen, H.F.; "Improve Indoor Position Localization Based on FFNN with PSO Optimization Algorithm". Int. J. Adv. Sci. Technol, 29 (4): 7246–7258, 2020.
[7] Stewart, W. J.; "Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling". 1st ed.; World Publishing Corporation: Beijing, China, 2009.
Youming, Z.; Xingchen, H.; "Application of Video Image Processing in Sports Action Recognition Based on Particle Swarm Optimization Algorithm". Prev. Med, 173: 107592, 2023.
https://doi.org/10.1016/j.ypmed.2023.107592
[8] Su, B.; Lin, W; Wang, J.; Rui, C.; "Sewage Treatment System for Improving Energy Efficiency Based on Particle Swarm Optimization Algorithm". Energy Rep.,8: 8701–8708, 2022.
[9] Solano-Rojas, B.J.; Villalón-Fonseca, R.; Batres, R.; "Micro Evolutionary Particle Swarm Optimization (MEPSO): A New Modified Metaheuristic". Syst. Soft Comput., 5: 200057, 2023.
https://doi:10.1016/j.sasc.2023.200057
[11] Banik, A.D.; Chaudhry, M.L.; Wittevrongel, S.; Bruneel, H.; "A Simple and Efficient Computing Procedure of the Stationary System-Length Distributions for GIX/D/c and D/c/1 Queues". Comput. Oper. Res., 138: 105564, 2022. https://doi.org/10.1016/j.cor.2021.105564
[12] Khalid, A.; Awad, K.; Mohamed, A.; Wagdy, A.; "Airport Terminal Building Capacity Evaluation Using Queuing System". Alexandria Eng. J., 61 (12): 10109–10118, 2022.
https://doi.org/10.1016/j.aej.2022.03.055
[13] Sheebha, A. D.J.; V P, N.; Abdulla, N.; Nihara, N.; Amiyan, K. S.S.; "An Android App: Virtual Queuing System for Public Distribution System". In: Proceedings of the 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, India, December 2021, Editors: Rajashree V. Biradar, Vijayakumar P., Sanjeev Kunte. IEEE: Piscataway, NJ, USA, 2021.
[14] Singh, S.K.; Acharya, S.K.; Cruz, F.R.B.; Da Costa Quinino, R.; "Bayesian Sample Size Determination in a Single-Server Deterministic Queueing System". Math. Comput. Simul., 187: 17–29, 2021. https://doi.org/10.1016/j.matcom.2021.02.010
[15] Boyd, S.; Vandenberghe, L.; "Convex Optimization". 1st ed.; Cambridge University Press: New York, NY, USA, 2013.
[16] Tiza, V.; Arun N.K.; Gopakumar P.; "Congestion management in power grids using multi-agent systems and particle swarm optimization". Franklin Open, 12: 100303,2025. https://doi.org/10.1016/j.fraope.2025.100303
[17] Shaymaa R. Th.; "Comparison Between the Simulated Prediction Methods of the Markov and Mixed Models". Al-Nahrain J.Sci., 27(4): 12-20, 2024. https://doi.org/10.22401/fw29aw06
[18] Ban, A. M.; Suhaib, A.; "Pattern Recognition Using Particle Swarm Optimization with Proposed a New Conjugate Gradient Parameter in Unconstrained Optimization". Al-Nahrain J. Sci., , 19(3): 138-147, 2016. https://doi.org/: 10.22401/JNUS.19.3.19.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Farah L. Joey, Wah June Leong, Dr. Chen Chuei Yee, Mohammad Lutfi Bin Othman

This work is licensed under a Creative Commons Attribution 4.0 International License.

.jpg)


