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Analysis of Queueing System with Dynamic Rating-Dependent Arrival Process and Price of Service

Author

Listed:
  • C. D’Apice

    (Dipartimento di Scienze Aziendali-Management & Innovation Systems, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084 Salerno, Italy)

  • A. N. Dudin

    (Department of Applied Mathematics and Computer Science, Belarusian State University, 4, Nezavisimosti Ave., 220030 Minsk, Belarus)

  • O. S. Dudina

    (Department of Applied Mathematics and Computer Science, Belarusian State University, 4, Nezavisimosti Ave., 220030 Minsk, Belarus)

  • R. Manzo

    (Department of Political and Communication Sciences, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084 Salerno, Italy)

Abstract

We consider a multi-server queueing system with a visible queue and an arrival flow that is dynamically dependent on the system’s rating. This rating reflects the level of customer satisfaction with the quality and price of the provided service. A higher rating implies a higher arrival rate, which motivates the service provider to increase the price of the service. A steady-state analysis of this system using the proposed mechanism for changing the rating and a threshold strategy for changing the price is performed. This is carried out via the consideration of a suitably constructed multidimensional Markov chain. The impact of the variation in the threshold defining the strategy for changing the price on the key performance indicators is numerically illustrated. The results can be used to make managerial decisions, leading to an increase in the effectiveness of system operations.

Suggested Citation

  • C. D’Apice & A. N. Dudin & O. S. Dudina & R. Manzo, 2024. "Analysis of Queueing System with Dynamic Rating-Dependent Arrival Process and Price of Service," Mathematics, MDPI, vol. 12(7), pages 1-20, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1101-:d:1371020
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    References listed on IDEAS

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    1. Sapana Sharma & Rakesh Kumar & Bhavneet Singh Soodan & Pradeep Singh, 2023. "Queuing models with customers' impatience: a survey," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 26(4), pages 523-547.
    2. Gad Allon & Awi Federgruen, 2008. "Service Competition with General Queueing Facilities," Operations Research, INFORMS, vol. 56(4), pages 827-849, August.
    3. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    4. Hwang, Johye & Gao, Long & Jang, Wooseung, 2010. "Joint demand and capacity management in a restaurant system," European Journal of Operational Research, Elsevier, vol. 207(1), pages 465-472, November.
    5. Fengfeng Huang & Pengfei Guo & Yulan Wang, 2019. "Cyclic Pricing When Customers Queue with Rating Information," Production and Operations Management, Production and Operations Management Society, vol. 28(10), pages 2471-2485, October.
    6. A. Gómez-Corral, 2006. "A bibliographical guide to the analysis of retrial queues through matrix analytic techniques," Annals of Operations Research, Springer, vol. 141(1), pages 163-191, January.
    7. Bei Wu & Lirong Cui & Chen Fang, 2020. "Generalized phase-type distributions based on multi-state systems," IISE Transactions, Taylor & Francis Journals, vol. 52(1), pages 104-119, January.
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