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Meta heuristic optimization of a batch arrival retrial queue with optional re-service and M-optional vacations

Author

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  • R. Harini

    (Vellore Institute of Technology)

  • K. Indhira

    (Vellore Institute of Technology)

Abstract

Queueing systems (QS) are essential for modelling and optimising a wide range of real-world scenarios because they effectively control the flow of entities through a system where there are queues. Addressing this, proposed QS comprises with batch arrival, retrial policy, optional re-service, and M-optional vacation alongside breakdown situations. The dynamics of the system are thoroughly investigated by utilizing the supplementary variable technique, which enables greater understanding of the system’s behaviour and performance. Furthermore, Adaptive Neuro-Fuzzy Inference System computation is used to properly validate the analytical results, improving the precision and dependability of the model’s predictions. Finally, in an effort to minimize operating efficiency, a variety of advanced cost optimization approaches have been employed to determine the system’s ideal cost structure. As a result, through this integrated approach, the proposed QS not only furnishes perceptions into operational dynamics but also furnishes practical methods to reduce costs and authenticate analytical conclusions, thereby enhancing queueing theory progressions and realistic applications across diverse fields.

Suggested Citation

  • R. Harini & K. Indhira, 2024. "Meta heuristic optimization of a batch arrival retrial queue with optional re-service and M-optional vacations," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4252-4282, September.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:9:d:10.1007_s13198-024-02418-4
    DOI: 10.1007/s13198-024-02418-4
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    References listed on IDEAS

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    1. Shweta Upadhyaya & Chetna Kushwaha, 2020. "Performance prediction and ANFIS computing for unreliable retrial queue with delayed repair under modified vacation policy," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 17(4), pages 437-466.
    2. Jain, Madhu & Dhibar, Sibasish, 2023. "ANFIS and metaheuristic optimization for strategic joining policy with re-attempt and vacation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 211(C), pages 57-84.
    3. Kumar, Anshul & Jain, Madhu, 2023. "Cost Optimization of an Unreliable server queue with two stage service process under hybrid vacation policy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 259-281.
    4. Shweta Upadhyaya & Richa Sharma & Divya Agarwal & Geetika Malik, 2023. "Convexity analysis and cost optimization of a retrial queue with Bernoulli vacation and delayed phase mending," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1671-1690, October.
    5. Shweta Upadhyaya, 2020. "Cost optimisation of a discrete-time retrial queue with Bernoulli feedback and starting failure," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 36(2), pages 165-196.
    6. Shweta Upadhyaya, 2015. "Admission control of bulk retrial feedback queue with K-optional vacations," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 7(2), pages 215-239.
    7. A. G. Pakes, 1969. "Some Conditions for Ergodicity and Recurrence of Markov Chains," Operations Research, INFORMS, vol. 17(6), pages 1058-1061, December.
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