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Quasi and metaheuristic optimization approach for service system with strategic policy and unreliable service

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Abstract

Demands for cost-efficient and just-in-time service systems have rapidly increased due to the present-day competitive resource allocation. We focus on optimizing policies for highly efficient service systems because customer congestion often arises from suboptimal policies rather than flawed arrangements. Quasi and metaheuristic optimization techniques are widely employed to establish cost-optimal service policies, mitigating customer congestion, primarily caused by unplanned policies or inadequate facilities. This article initially introduces a notion of unreliable service and the F-policy for stochastic modeling of finite capacity customer service systems. Next, we utilize the recently-developed and proficient Grey Wolf Optimizer, a metaheuristic approach, along with the Quasi-Newton method, to determine the optimal values of decision parameters for a cost-efficient service systems. This is achieved through extensive numerical experiments that encompass diverse service characteristics, customer behavior, and performability measures. The results emphasizes the importance of both preventive and corrective actions for enhancing service system efficiency. Our findings also highlight the practicality of the Grey Wolf Optimization approach and stochastic modeling in achieving efficient policies and optimizing performance for the studied service model. In general, the F-policy is widely adopted for controlling queueing systems across various industries such as telecommunications, transportation, and healthcare, where maintaining reasonable wait times, service levels, and system stability is crucial. This article contributes to the mathematical modeling of this approach. Nonetheless, further research is needed to validate and simulate these findings in industrial settings.

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Funding

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. The first author (MD) extends his sincere thanks to the funding agency CSIR-UGC, India, for the financial support (SRF/NET 1081/(CSIR-UGC NET DEC. 2018)).

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Authors and Affiliations

  1. Department of Mathematics, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, Rajasthan, 333 031, India

    Mahendra Devanda, Suman Kaswan & Chandra Shekhar

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  1. Mahendra Devanda
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  2. Suman Kaswan
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  3. Chandra Shekhar
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Correspondence to Chandra Shekhar.

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Devanda, M., Kaswan, S. & Shekhar, C. Quasi and metaheuristic optimization approach for service system with strategic policy and unreliable service. J Ambient Intell Human Comput 15, 2295–2315 (2024). https://doi.org/10.1007/s12652-024-04756-4

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  • DOI: https://doi.org/10.1007/s12652-024-04756-4

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