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Optimization of a stochastic model having erratic server with immediate or delayed repair

Author

Listed:
  • Radhika Agarwal

    (Amity University)

  • Divya Agarwal

    (Amity University)

  • Shweta Upadhyaya

    (Amity University)

  • Izhar Ahmad

    (King Fahd University of Petroleum and Minerals)

Abstract

The thought to put forward a queuing model proposed in this work was its pertinence in everyday life wherever we can see the uses of computing and networking systems. Industrial software developers and system managers can consider the results of the model to evolve their system for better results. Here we present a novel queueing model having erratic server with delayed repair and balking. Two distinct breakdowns i.e. active and passive breakdown for the system are also considered with their respective amendments. This model is closely related with the smooth functioning of the system during some internal faults (virus attack, electricity failures etc.). The performance indicators which are utilized in enhancing the service standards are obtained using supplementary variable technique. Using ANFIS soft computing technique we have compared the analytical results with those of neuro fuzzy results. Furthermore single and bi-objective minimization problems are considered and minima is obtained using particle swarm optimization and multi objective genetic algorithm respectively. Also, the minimization problems are shown as a convex programming problem to ensure the global optimality of the result. The proposed approach makes it conceivable to accomplish a relevant harmony between operational expenses and administration quality.

Suggested Citation

  • Radhika Agarwal & Divya Agarwal & Shweta Upadhyaya & Izhar Ahmad, 2023. "Optimization of a stochastic model having erratic server with immediate or delayed repair," Annals of Operations Research, Springer, vol. 331(2), pages 605-628, December.
    • Handle: RePEc:spr:annopr:v:331:y:2023:i:2:d:10.1007_s10479-022-04804-2
    DOI: 10.1007/s10479-022-04804-2
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