A queueing-inventory model with skeptical and trusting customers

Hanukov, Gabi

doi:10.1007/s10479-022-04936-5

Cited by 2 publications

(1 citation statement)

References 67 publications

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“…In the M/M/1 queue and retrial queue, the authors, respectively, studied the equilibrium strategies of customers and the effect of information heterogeneity on throughput and social welfare. Wang and Fang (2022) [14] took the heterogeneity of priority awareness as a classification criterion; Wang and Sun (2022) [15] classified customers according to customer service experience, according to whether customers choose to stay on-site when the system provides services; and Hanukov et al (2023) [16] also divided customers into questioning customers and trusting customers; then, the authors studied the customer equilibrium strategy and system optimization decision-making in the priority queueing system, online service queueing system, queue-inventory system and other related systems.…”

Section: Introductionmentioning

confidence: 99%

Strategic Queueing Behavior of Two Groups of Patients in a Healthcare System

Liu,

Jiang

et al. 2024

Mathematics

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Long waiting times and crowded services are the current medical situation in China. Especially in hierarchic healthcare systems, as high-quality medical resources are mainly concentrated in comprehensive hospitals, patients are too concentrated in these hospitals, which leads to overcrowding. This paper constructs a game-theoretical queueing model to analyze the strategic queueing behavior of patients. In such hospitals, patients are divided into first-visit and referred patients, and the hospitals provide patients with two service phases of “diagnosis” and “treatment”. We first obtain the expected sojourn time. By defining the patience level of patients, the queueing behavior of patients in equilibrium is studied. The results suggest that as long as the patients with low patience levels join the queue, the patients with high patience levels also join the queue. As more patients arrive at the hospitals, the queueing behavior of patients with high patience levels may have a negative effect on that of patients with low patience levels. The numerical results also show that the equilibrium behavior deviates from a socially optimal solution; therefore, to reach maximal social welfare, the social planner should adopt some regulatory policies to control the arrival rates of patients.

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