Dynamic Queueing Model for Smart Manufacturing: A Priority-Based Performance Study
DOI:
https://doi.org/10.26713/cma.v16i2.3095Keywords:
Dynamic Priority Queueing Model (DPQM), Priority function, Scheduling algorithm, Manufacturing systems, Resource utilization, Lead timeAbstract
In this paper, we present a Dynamic Priority Queueing Model (DPQM), which effectively solves the problems of traditional queueing models in complex priority scheduling situations in manufacturing systems. The DPQ paradigm uses priority functions, time-based queueing, and advanced scheduling algorithms to schedule tasks elegantly. The priority function swiftly prioritizes tasks based on urgency, relevance, and resource demands, adapting to changing circumstances. The~suggested \(M(t)/G(t)/c\) queueing model extends the \(M/G/c\) model for real-world applications with time-dependent task arrivals and service activities. The model validation and implementation process enable efficient calculations of priority values, lead time, tardiness, usage, and efficiency. The average lead time is 179 minutes, tardiness is 106 minutes, and resource utilization is 100%. The scheduling efficiency improves by 17.6% by effectively sequencing activities to match system priorities. The validation shows that the model prioritizes work and properly maps changing parameters compared to traditional queueing systems. The results indicate that improving adaptive scheduling, real-time feedback systems, and using multiple servers can maximize resource utilization to generate high throughput and eliminate work delays. This approach serves as a robust and adaptable strategy for dynamic scheduling, thoughtfully considering priorities within complex manufacturing environments.
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References
S. Aalto and Z. Scully, Minimizing the mean slowdown in the M/G/1 queue, Queueing Systems 104 (2023), 187 – 210, DOI: 10.1007/s11134-023-09888-6.
E. Aggarwal and B. K. Mohanty, Hesitant fuzzy sets with non-uniform linguistic terms: An application in multi-attribute decision making, International Journal of Mathematics in Operational Research 24(1) (2023), 1 – 28, DOI: 10.1504/IJMOR.2021.10044478.
M. Amjath, L. Kerbache, A. Elomri and J. M. Smith, Queueing network models for the analysis and optimisation of material handling systems: A systematic literature review, Flexible Services and Manufacturing Journal 36 (2024), 668 – 709, DOI: 10.1007/s10696-023-09505-x.
M. Amjath, L. Kerbache, J. M. Smith and A. Elomri, Optimisation of buffer allocations in manufacturing systems: A study on intra and outbound logistics systems using finite queueing networks, Applied Science 13(17) (2023), 9525, DOI: 10.3390/app13179525.
C. Chen and L. K. Tiong, Using queueing theory and simulated annealing to design the facility layout in an AGV-based modular manufacturing system, International Journal of Production Research 57(17) (2019), 5538 – 5555, DOI: 10.1080/00207543.2018.1533654.
S. Dharmaraja and S. Kapoor, On the time-dependent solution of fluid models driven by an M/M/1 queue using a probabilistic approach, International Journal of Operational Research 46(1) (2023), 65 – 72, DOI: 10.1504/IJOR.2023.128580.
C. Gongshan, N. Yuan, Y. Lu and G. Yudong, On production process optimization based on queueing theory-take enterprise a as an example, in: Proceedings of the 6th International Conference on Humanities and Social Science Research (ICHSSR 2020), X. Du, C. Huang and Y. Zhong (editors), (2020).
P. V. Laxmi and A. A. George, Transient and steady state analysis of M/M∧([b])/1 queue with second optional service, Journal of Industrial and Production Engineering 39(4) (2022), 306–316, DOI: 10.1080/21681015.2021.1989065.
S. Mehra and P. G. Taylor, Open networks of infinite server queues with non-homogeneous multivariate batch Poisson arrivals, Queueing Systems 105 (2023), 171 – 187, DOI: 10.1007/s11134-023-09891-x.
P. S. Murdapa, I. N. Pujawan, P. D. Karningsih and A. H. Nasution, Single stage queueing/manufacturing system model that involves emission variable, IOP Conference Series: Materials Science and Engineering 337 (2018), 012008, DOI: 10.1088/1757-899X/337/1/012008.
L. Rece, S. Vlase, D. Ciuiu, G. Neculoiu, S. Mocanu and A. Modrea, Queueing theory-based mathematical models applied to enterprise organization and industrial production optimization, Mathematics 10(14) (2022), 2520, DOI: 10.3390/math10142520.
B. Saini, D. Singh and K. C. Sharma, Application of queueing theory to analyze the performance metrics of manufacturing systems, Asian Research Journal of Mathematics 20(12) (2024), 84 – 95, DOI: 10.9734/arjom/2024/v20i12876.
B. Saini, D. Singh and K. C. Sharma, Exploring the role of queueing theory in manufacturing: An analytical study, International Research Journal on Advanced Engineering and Management 2(3) (2024), 256 – 266, DOI: 10.47392/IRJAEM.2024.0039.
S. Ülkü, C. Hydock and S. Cui, Making the wait worthwhile: Experiments on the effect of queueing on consumption, Management Science 66(3) (2019), 1149 – 1171, DOI: 10.1287/mnsc.2018.3277.
K. Vorhölter, G. Greefrath, R. B. Ferri, D. Leib and S. Schukajlow, Mathematical modelling, in: Traditions in German-Speaking Mathematics Education Research (ICME-13 Monographs), H. Jahnke and L. Hefendehl-Hebeker (editors), Springer, Cham., (2019), DOI: 10.1007/978-3-030-11069-7_4.
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