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Considering an M/G/1 queueing model with two types of breakdowns, i.e., active and passive breakdowns with immediate and delayed repairs respectively in this paper, global optimal values of salient measures to study the system are determined. The results obtained can be used in amplifying the serving standards of various sectors like banks, hospitals, telecommunication, etc. ANFIS (adaptive neuro-fuzzy inference system) is used to verify the influence of various boundary parameters on some performance measures. The cost function and the minimum cost for the system are obtained using two distinct optimization techniques, i.e., Firefly Algorithm (FA) and Cuckoo Search (CS). Furthermore, the minimum cost and minimum awaiting time of the system are estimated using multi objective genetic algorithm (MOGA). To ensure that the proposed solution is globally optimal, the minimization problem is unveiled as a convex programming problem.

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Reckoning of Optimal Parameters of M/G/1 Retrial Model with Active and Passive Breakdowns Using Optimization Techniques

  • 章节

  • 发表日期：

    2025年

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  • Radhika Agarwal,
  • Divya Agarwal

摘要

Considering an M/G/1 queueing model with two types of breakdowns, i.e., active and passive breakdowns with immediate and delayed repairs respectively in this paper, global optimal values of salient measures to study the system are determined. The results obtained can be used in amplifying the serving standards of various sectors like banks, hospitals, telecommunication, etc. ANFIS (adaptive neuro-fuzzy inference system) is used to verify the influence of various boundary parameters on some performance measures. The cost function and the minimum cost for the system are obtained using two distinct optimization techniques, i.e., Firefly Algorithm (FA) and Cuckoo Search (CS). Furthermore, the minimum cost and minimum awaiting time of the system are estimated using multi objective genetic algorithm (MOGA). To ensure that the proposed solution is globally optimal, the minimization problem is unveiled as a convex programming problem.