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

2019 ◽  
Vol 7 (4A) ◽  
pp. 17-23
Author(s):  
Noor Hidayah Mohd Zaki ◽  
Aqilah Nadirah Saliman ◽  
Nur Atikah Abdullah ◽  
Nur Su Ain Abu Hussain ◽  
Norani Amit
Keyword(s):  
Queuing Theory ◽  
Theory Model ◽  
Queuing Model

scholarly journals THE CONTINUOUS-TIME TIME BERNULLI PROCESS

2017 ◽  
pp. 79-87
Author(s):  
Valery Pavsky ◽  
Valery Pavsky ◽  
Kirill Pavsky ◽  
Kirill Pavsky ◽  
Svetlana Ivanova ◽  
...  
Keyword(s):  
Poisson Process ◽  
Poisson Distribution ◽  
Continuous Time ◽  
Queuing Theory ◽  
Probability Space ◽  
Fixed Time ◽  
Theory Model ◽  
Bernoulli Process ◽  
Queuing Model ◽  
First Case

A random Bernoulli process with continuous time and a finite number of states (random events) is proposed. The process is obtained by two mutually complementary methods - directly from the Poisson process with an intensity parameter that depends on time and methods of queuing theory, from a queuing system with two parameters. In the first case, the process was formalized on a probability space with measure, as a measurable function of time. The intensity of the Poisson process was considered as a measure. The Bernoulli process for each fixed time was obtained as a conditional distribution from a suitable Poisson distribution. The parameter of the Poisson distribution was determined from the differential equation, in the formulation of which the approximation of the Bernoulli formula by the Poisson formula was essentially used. In the second method, standard methods of queuing theory were used. A two- parameter queuing model was formulated in which for all customer flows the time between occurrence of neighboring customers was a random value satisfying the exponential law. The model was formalized by a system of differential equations, whose analytical solution represented the continuous-time Bernoulli process. In finding solutions, the method of generating functions was used. It is of interest to derive the Bernoulli process both from the probability space constructed for the Poisson process and from the queuing theory model. The authors believe that the proposed process can be generalized to a wider class of functions than that used in the work, down to measurable ones. The possibilities for the practical application of the continuous-time Bernoulli process will undoubtedly be expanded, since its discrete analog is well known in many fields of science and technology.

Evaluation of subway passengers transfer hub congestion based on queuing model and TOPSIS method

2021 ◽  
pp. 1-22
Author(s):  
Xiaokun Wang ◽  
Dong Ni
Keyword(s):  
Queuing Theory ◽  
Evaluation Model ◽  
Flow Characteristics ◽  
Distribution Area ◽  
Flow Density ◽  
Topsis Method ◽  
Queuing Model ◽  
Service Facility ◽  
Passenger Flow ◽  
Topsis Technique

To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

2021 ◽  
pp. 036119812110302
Author(s):  
Chao Wang ◽  
Weijie Chen ◽  
Yueru Xu ◽  
Zhirui Ye
Keyword(s):  
Approximation Method ◽  
Service Time ◽  
Diffusion Approximation ◽  
Coefficient Of Variation ◽  
Queuing Theory ◽  
Queuing Model ◽  
Bus Stop ◽  
Proposed Model ◽  
Bus Stops ◽  
Bus Service

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

A spatial queuing model for the location decision of emergency medical vehicles for pandemic outbreaks: the case of Za'atari refugee camp

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Felix Blank
Keyword(s):  
Queuing Theory ◽  
Emergency Service ◽  
Performance Metrics ◽  
Service System ◽  
Refugee Camps ◽  
Location Decision ◽  
Stress Tests ◽  
Queuing Model ◽  
Content Type ◽  
Emergency Medical

PurposeRefugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.Design/methodology/approachA spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.FindingsThe derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.Research limitations/implicationsSome parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.Practical implicationsThe model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.Originality/valueThe study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

scholarly journals Applications of Queuing Theory in Quantitative Business Analysis

2020 ◽  
pp. 253-257
Keyword(s):  
Quantitative Analysis ◽  
Waiting Time ◽  
Queuing Theory ◽  
Single Channel ◽  
Analysis Approach ◽  
Single Server ◽  
Expected Number ◽  
Queuing Model ◽  
Infinite Population ◽  
Expected Waiting Time

Queuing Theory provides the system of applications in many sectors in life cycle. Queuing Structure and basic components determination is computed in queuing model simulation process. Distributions in Queuing Model can be extracted in quantitative analysis approach. Differences in Queuing Model Queue discipline, Single and Multiple service station with finite and infinite population is described in Quantitative analysis process. Basic expansions of probability density function, Expected waiting time in queue, Expected length of Queue, Expected size of system, probability of server being busy, and probability of system being empty conditions can be evaluated in this quantitative analysis approach. Probability of waiting ‘t’ minutes or more in queue and Expected number of customer served per busy period, Expected waiting time in System are also computed during the Analysis method. Single channel model with infinite population is used as most common case of queuing problems which involves the single channel or single server waiting line. Single Server model with finite population in test statistics provides the Relationships used in various applications like Expected time a customer spends in the system, Expected waiting time of a customer in the queue, Probability that there are n customers in the system objective case, Expected number of customers in the system

Design of TVM Based on Parallel Virtual Queuing Theory

2014 ◽  
Vol 1030-1032 ◽  
pp. 2195-2198
Author(s):  
Xu Guang Min ◽  
Tao Wan ◽  
Jun Fang ◽  
Shan Liu
Keyword(s):  
Queuing Theory ◽  
Queuing Model

From what has been discussed above, the design of parallel virtual queuing, means that the passenger chooses the trains, the time, departures and terminals, seat types on TVM by blackening a ticket purchasing card. All the job off the TVM can be done by all the passengers all at once, which is equal to every passenger is being served at the counter (TVM), and thus the queuing time are greatly reduced in peak hours. Therefore, the queuing is equal to “Parallel Virtual Queuing”, and the equivalent queuing model is M/M/∞/∞/∞/FCFS.

Queuing Theory Accurately Models the Need for Critical Care Resources

2004 ◽  
Vol 100 (5) ◽  
pp. 1271-1276 ◽  
Author(s):  
Michael L. McManus ◽  
Michael C. Long ◽  
Abbot Cooper ◽  
Eugene Litvak
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Queuing Theory ◽  
Patient Flow ◽  
Queuing Model ◽  
Hospital Administrators ◽  
Scarce Resources ◽  
Resource Needs ◽  
Proper Number ◽  
Predicted Values

Background Allocation of scarce resources presents an increasing challenge to hospital administrators and health policy makers. Intensive care units can present bottlenecks within busy hospitals, but their expansion is costly and difficult to gauge. Although mathematical tools have been suggested for determining the proper number of intensive care beds necessary to serve a given demand, the performance of such models has not been prospectively evaluated over significant periods. Methods The authors prospectively collected 2 years' admission, discharge, and turn-away data in a busy, urban intensive care unit. Using queuing theory, they then constructed a mathematical model of patient flow, compared predictions from the model to observed performance of the unit, and explored the sensitivity of the model to changes in unit size. Results The queuing model proved to be very accurate, with predicted admission turn-away rates correlating highly with those actually observed (correlation coefficient = 0.89). The model was useful in predicting both monthly responsiveness to changing demand (mean monthly difference between observed and predicted values, 0.4+/-2.3%; range, 0-13%) and the overall 2-yr turn-away rate for the unit (21%vs. 22%). Both in practice and in simulation, turn-away rates increased exponentially when utilization exceeded 80-85%. Sensitivity analysis using the model revealed rapid and severe degradation of system performance with even the small changes in bed availability that might result from sudden staffing shortages or admission of patients with very long stays. Conclusions The stochastic nature of patient flow may falsely lead health planners to underestimate resource needs in busy intensive care units. Although the nature of arrivals for intensive care deserves further study, when demand is random, queuing theory provides an accurate means of determining the appropriate supply of beds.

scholarly journals Effective school cooperative-mart queuing system

2017 ◽  
Vol 13 (4-1) ◽  
pp. 412-415
Author(s):  
Ahmad Ridhuan Hamdan ◽  
Ruzana Ishak ◽  
Mohd Fais Usop
Keyword(s):  
Queuing Theory ◽  
Service Level ◽  
Queuing System ◽  
Queuing Model ◽  
Effective School ◽  
Service Centre ◽  
Large Numbers ◽  
Female Data ◽  
Waiting Line ◽  
Number Of Customers

Queuing Theory is a branch of knowledge in operation research that concerning the analysis of queues when a customer arrives at a service centre and shall queue in a line to get some service. The theory pays attention to how organizations can serve a large number of customers who demand a quality services and a queue of customers waiting to be served. Eventually, the store owners have to attend to large numbers of customers at a time have attempted to measure and manage queues to reduce the customer procession time. Besides, to increase sales and profit, productivity and operation efficiency, satisfaction levels and customer loyalty in using the service provided. In line to the situation, this study is to determine the effectiveness of the waiting line using Queuing Theory at cooperative-mart. Until today, no research conducted about school cooperatives-mart to observe and solve the massive inflow of customers at lines at a given time especially during lunch hour. The purposes of this study are to determine the customer congestion at the payment counter and to propose the effective queuing system at Cooperative-mart. Waiting and services times of customers at cooperative-mart is studied in three times period that to be considered as peak hours in two types of counter which are for male and female.  Data collection was observed by using queuing theory and the M/M/1/∞/∞ queuing model has been implemented.  The results show that for optimum service level, the counter must be changed from one to two counters each side.  The summary and finding of the study shall be used as guideline for the management of cooperative-mart in deciding improvement of its operation. 

scholarly journals Influence of various application types on the performance of LTE mobile networks

2020 ◽  
Vol 10 (5) ◽  
pp. 5420
Author(s):  
Adel Agamy ◽  
Ahmed M. Mohamed
Keyword(s):  
Cellular Networks ◽  
Mobile Networks ◽  
Queuing Theory ◽  
Performance Model ◽  
Mobile Internet ◽  
Queuing Model ◽  
Lte Networks ◽  
Wireless Device ◽  
Self Similar ◽  
Internet Networks

Modern mobile internet networks are becoming heavier and denser. Also it is not regularly planned, and becoming more heterogeneous. The explosive growth in the usage of smartphones poses numerous challenges for LTE cellular networks design and implementation. The performance of LTE networks with bursty and self-similar traffic has become a major challenge. Accurate modeling of the data generated by each connected wireless device is important for properly investigating the performance of LTE networks. This paper presents a mathematical model for LTE networks using queuing theory considering the influence of various application types. Using sporadic source traffic feeding to the queue of the evolved node B and with the exponential service time assumption, we construct a queuing model to estimate the performance of LTE networks. We use the performance model presented in this paper to study the influence of various application categories on the performance of LTE cellular networks. Also we validate our model with simulation using NS3 simulator with different scenarios.

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