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Makhdoom, Iman. (1402). A new optimum statistical estimation of the traffic intensity parameter for the M/M/1/K queuing model based on fuzzy and non-fuzzy criteria. سامانه مدیریت نشریات علمی, 2(1), 163-184. doi: 10.22054/jdsm.2024.79643.1048
Iman Makhdoom. "A new optimum statistical estimation of the traffic intensity parameter for the M/M/1/K queuing model based on fuzzy and non-fuzzy criteria". سامانه مدیریت نشریات علمی, 2, 1, 1402, 163-184. doi: 10.22054/jdsm.2024.79643.1048
Makhdoom, Iman. (1402). 'A new optimum statistical estimation of the traffic intensity parameter for the M/M/1/K queuing model based on fuzzy and non-fuzzy criteria', سامانه مدیریت نشریات علمی, 2(1), pp. 163-184. doi: 10.22054/jdsm.2024.79643.1048
Makhdoom, Iman. A new optimum statistical estimation of the traffic intensity parameter for the M/M/1/K queuing model based on fuzzy and non-fuzzy criteria. سامانه مدیریت نشریات علمی, 1402; 2(1): 163-184. doi: 10.22054/jdsm.2024.79643.1048

A new optimum statistical estimation of the traffic intensity parameter for the M/M/1/K queuing model based on fuzzy and non-fuzzy criteria

Journal of Data Science and Modeling
دوره 2، شماره 1 - شماره پیاپی 3، اسفند 2023، صفحه 163-184    اصل مقاله (316.48 K)
نوع مقاله: original
شناسه دیجیتال (DOI): 10.22054/jdsm.2024.79643.1048
نویسنده
Iman Makhdoom*
Payame Noor University
چکیده
This article focuses on the M/M/ 1 /K queuing model. In this model, the inter-arrival times of
customers to the system are random variables with an exponential distribution parameterized by λ , and
the service times of customers are random variables with an exponential distribution parameterized by
µ . We aim to estimate the traffic intensity parameter of this model using Bayesian, E-Bayesian, and
hierarchical Bayesian methods. These methods utilize the entropy loss function and an appropriate prior
distribution for the independent parameters λ and µ . Additionally, we employ the shrinkage-based
maximum likelihood estimation method to obtain the parameter estimates. To determine the desired
traffic intensity parameter estimate, we introduce a decision criterion based on a cost function, and
a fuzzy criterion called the Average Customer Satisfaction Index (ACSI). The goal is to select the
estimation with a higher ACSI index. To facilitate understanding, we compare this estimation using the
Monte Carlo simulation method and two numerical examples based on the ACSI index.
کلیدواژه‌ها
M/M/1/K queuing model؛ traffic intensity parameter؛ cost function؛ ACSI
آمار
تعداد مشاهده مقاله: 21
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