Comparison of Queuing Performance Using Queuing Theory Model and Fuzzy Queuing Model at Check-in Counter in Airport

Zaki, Noor Hidayah Mohd; Saliman, Aqilah Nadirah; Abdullah, Nur Atiqah Sia; Hussain, Nur Su Ain Abu; Amit, Norani

doi:10.13189/ms.2019.070703

Cited by 16 publications

(7 citation statements)

References 7 publications

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“…Let us emphasize the difference between the approach and results of this paper and the studies on continuous-time random processes with discrete fuzzy states. For example, fuzzy queueing systems were discussed in [9][10][11][12]; stochastic fuzzy dynamic automatic control systems were considered in [13,14]. At the same time, stationary fuzzy random processes and their covariance functions were not addressed in the publications cited above.…”

Section: Khatskevichmentioning

confidence: 99%

“…Therefore, according to (11), the spectral density of the FRP X(t) of the triangular form (ξ 1 (t), ξ 2 (t), ξ 3 (t)) is given by…”

Section: The Spectral Density Of a Stationary Frp The Generalized Wie...mentioning

confidence: 99%

“…The next result is valid by definition (11) and the properties of the spectral densities of real stationary random processes (see [16,Chapter 24]).…”

Section: The Spectral Density Of a Stationary Frp The Generalized Wie...mentioning

confidence: 99%

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On the Transformation of a Stationary Fuzzy Random Process by a Linear Dynamic System

Khatskevich

2024

ARC

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In this paper, stationary random processes with fuzzy states are studied. The properties of their numerical characteristics—fuzzy expectations, expectations, and covariance functions—are established. The spectral representation of the covariance function, the generalized Wiener–Khinchin theorem, is proved. The main attention is paid to the problem of transforming a stationary fuzzy random process (signal) by a linear dynamic system. Explicitform relationships are obtained for the fuzzy expectations (and expectations) of input and output stationary fuzzy random processes. An algorithm is developed and justified to calculate the covariance function of a stationary fuzzy random process at the output of a linear dynamic system from the covariance function of a stationary input fuzzy random process. The results rest on the properties of fuzzy random variables and numerical random processes. Triangular fuzzy random processes are considered as examples.

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Section: Khatskevichmentioning

confidence: 99%

“…Therefore, according to (11), the spectral density of the FRP X(t) of the triangular form (ξ 1 (t), ξ 2 (t), ξ 3 (t)) is given by…”

Section: The Spectral Density Of a Stationary Frp The Generalized Wie...mentioning

confidence: 99%

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On the Transformation of a Stationary Fuzzy Random Process by a Linear Dynamic System

Khatskevich

2024

ARC

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“…Different queuing models have been developed to observe the queuing behaviour of a system and optimize the queue length. Hidayah Zaki et al, (2019) observed the performance by measuring the average number of customers in the system (Ls), the average number of customers waiting in the queue (Lq), the average time a customer spends in the system (Ws), and the average time a customer spends waiting in the queue (Wq) of an airline company's queuing system using Fuzzy Queuing Model. This model provides fuzzy numbers in a range that guides the operator to ensure maximum output in a specific period by minimizing the service time.…”

Section: Literature Reviewmentioning

confidence: 99%

Formulation and Performance Assessment for Multiple Server Queueing Models

Alam¹,

Azrin²,

U³

et al. 2023

Preprint

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Queueing theory has grown in prominence as it provides the numerical foundation for decision-making assessment. Queueing models with multiple servers provide extensive decision-making data, which is critical for evaluating a server's performance. The purpose of this study is to formulate multiple server queueing models and provides a performance assessment to evaluate the appropriate models. A three-phased structured approach has been used to model and analyze performance for multiple server queueing models. The "Multiple Server Finite Queue Length Infinite Queue Population Model" is most desirable for a customer who has to wait for less time (approximately 43.14%) in the system as well as in the queue (approximately 62.16%) and reduces the system length by approximately 47.02% and the queue length by approximately 64.76%. However, the "Multiple Server Infinite Queue Length Infinite Queue Population Model" is preferable from a managerial perspective, as the "Multiple Server Finite Queue Length Infinite Queue Population Model" has fewer customers in the system, indicating a loss from a managerial standpoint. When the arrival rate of customers, service rate, and the number of servers are increased, length of the system and queue remain nearly constant, whereas the waiting time in the system nearly doubles for both queueing models. The paper develops a performance evaluation of the “Multiple Server Infinite Queue Length Infinite Queue Population Model” and “Multiple Server Finite Queue Length Infinite Queue Population Model”, which are capable of adapting to an unpredictable decision in any service system. Furthermore, this study provides decision-makers with a perspective-based evaluation of the mentioned servers, with a focus on a manager and a customer.

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“…Definition 2.9. The extension of fuzzy arithmetic operations of Ming Ma et al [18] to the set of triangular intuitionistic fuzzy numbers based upon both location indices and functions of fuzziness indices. The location indices number is taken in the regular arithmetic while the functions of fuzziness indices are assumed to follow the lattice rule which is the least upper bound in the lattice I ′ …”

Section: ( ) ( )mentioning

confidence: 99%

Comparison of Non-Preemptive Priority Queuing Performance Using Fuzzy Queuing Model and Intuitionistic Fuzzy Queuing Model with Different Service Rates

Aarthi¹,

Shanmugasundari²

2022

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Comparison of Queuing Performance Using Queuing Theory Model and Fuzzy Queuing Model at Check-in Counter in Airport

Cited by 16 publications

References 7 publications

On the Transformation of a Stationary Fuzzy Random Process by a Linear Dynamic System

On the Transformation of a Stationary Fuzzy Random Process by a Linear Dynamic System

Formulation and Performance Assessment for Multiple Server Queueing Models

Comparison of Non-Preemptive Priority Queuing Performance Using Fuzzy Queuing Model and Intuitionistic Fuzzy Queuing Model with Different Service Rates

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