scholarly journals Single Server Queuing Model to Determine the Patient Flow Process in a Township Hospital

2019 ◽  
Vol 8 (12S) ◽  
pp. 488-489
Keyword(s):  
Queuing Theory ◽  
Tamil Nadu ◽  
Patient Flow ◽  
Health Centre ◽  
Single Server ◽  
Queuing Model ◽  
Flow Process ◽  
Health Care Centre ◽  
Township Hospital ◽  
The Relationship

This research aims to analyse the use of queuing theory in the two branches of a local township health care centre located in a small township in Tamil Nadu, India. A scenario from the out-patient departments of the aforesaid centre shows the relationship between the different variables operating the system. The focus of this research is also to provide some insights for improving the efficiency of the medical centre through the queuing model. The research concludes that the Queuing system at branch A of the health centre is 93% efficient and at Branch B it is 73%efficient.

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

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 Transient Solution of a Single Server Queuing Model with Correlated Reneging Using Runge-Kutta Method

2020 ◽  
Vol 5 (5) ◽  
pp. 886-896
Author(s):  
Rakesh Kumar ◽  
Bhavneet Singh Soodan
Keyword(s):  
Queuing Theory ◽  
Real Life ◽  
System Size ◽  
Single Server ◽  
Kutta Method ◽  
Transient Solution ◽  
Queuing Model ◽  
Runge Kutta Method ◽  
Runge Kutta ◽  
Expected Waiting Time

In this paper, the concept of correlated reneging is introduced in queuing theory. The reneging considered so far is dependent on system size, but there are many real life situations where customers may renege due to exogenous factors other than the state of the system. Further, the reneging of customer may induce the other customers to renege at two successive time points. Such reneging is called correlated reneging. An M/M/1/K queuing model with correlated reneging is studied. Runge-Kutta method of fourth order is presented to obtain the transient solution of the model. Some performance measures like expected system size and expected waiting time in the system are studied.

scholarly journals Analysis of the organization of operation of engineering systems of residential buildings based on a queuing model with interruptions and close-downs

2020 ◽  
Vol 8 (4) ◽  
pp. 77-82
Author(s):  
Georgij Afanasiev
Keyword(s):  
Queuing Theory ◽  
Residential Buildings ◽  
Probabilistic Methods ◽  
Main Element ◽  
Single Server ◽  
Queuing Model ◽  
Inspection Work ◽  
Rational Organization ◽  
The One ◽  
Technical Operation

In resent years has been increasing interest for application of probabilistic methods, in particular queueing theory for estimation of activities of managing companies of residential buildings. Maintenance of residential buildings is a set of measures that ensure the highest reliability of all elements and systems of a building. The main element of the technical operation of residential buildings is a system of scheduled prophylactic inspections and repairs. However even with its rational organization, there is always a positive probability of failure of building elements, which depends not only on the aging factors of the structure. The goal of the managing company is, on the one hand, to prevent the formation of a too long queue of emergency calls, and on the other, to complete all planned prophylactic maintenance work. As a mathematical model we consider a single-server queue with vacations and close-down periods. The service team can start a scheduled preventive repair only when all request for sudden mailfunctions are satisfied. This period we call vacation. There is a close-down period before vacation. This time period is required for preparation and organization of the prophylactic and inspection work. Based on the methods of the queuing theory, the system’s characteristics that determine the quality of its work, as well as the boundaries of the change of parameters at which the system copes with the work from the standpoint of a particular criterion are defined.

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 Performance Modelling of Health-care Service Delivery in Adekunle Ajasin University, Akungba-Akoko, Nigeria Using Queuing Theory

2020 ◽  
pp. 119-127
Author(s):  
Orimoloye Segun Michael
Keyword(s):  
Health Care ◽  
Service Delivery ◽  
Queuing Theory ◽  
Health Care Service ◽  
Health Centre ◽  
Arrival Rate ◽  
Queuing System ◽  
Service Rate ◽  
Suitable Model ◽  
The Mean

The queuing theory is the mathematical approach to the analysis of waiting lines in any setting where arrivals rate of the subject is faster than the system can handle. It is applicable to the health care setting where the systems have excess capacity to accommodate random variation. Therefore, the purpose of this study was to determine the waiting, arrival and service times of patients at AAUA Health- setting and to model a suitable queuing system by using simulation technique to validate the model. This study was conducted at AAUA Health- Centre Akungba Akoko. It employed analytical and simulation methods to develop a suitable model. The collection of waiting time for this study was based on the arrival rate and service rate of patients at the Outpatient Centre. The data was calculated and analyzed using Microsoft Excel. Based on the analyzed data, the queuing system of the patient current situation was modelled and simulated using the PYTHON software. The result obtained from the simulation model showed that the mean arrival rate of patients on Friday week1 was lesser than the mean service rate of patients (i.e. 5.33> 5.625 (λ > µ). What this means is that the waiting line would be formed which would increase indefinitely; the service facility would always be busy. The analysis of the entire system of the AAUA health centre showed that queue length increases when the system is very busy. This work therefore evaluated and predicted the system performance of AAUA Health-Centre in terms of service delivery and propose solutions on needed resources to improve the quality of service offered to the patients visiting this health centre.

scholarly journals Strengthening management of non-communicable diseases in primary care, Malawi: A short report

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Amos Mailosi ◽  
Christina Miller ◽  
Catherine Hodge ◽  
Serah Msimuko
Keyword(s):  
Primary Care ◽  
Family Medicine ◽  
Team Building ◽  
Health Centre ◽  
Communicable Diseases ◽  
Short Report ◽  
Medicine Department ◽  
Non Communicable Diseases ◽  
The Relationship ◽  
Initial Focus

Within the community-orientated primary care module for training family physicians at the Kamuzu University of Health Sciences in Malawi, a relationship was formed between Nkhoma Mission Hospital’s Family Medicine Department and the Diamphwe Community Health Centre (HC) to strengthen the continuity of healthcare and capacity team building. The initial focus was on improving the management of hypertension and diabetes in terms of diagnosis, tracking of the patients in a registry and timely referral to secondary care facilities The relationship has received positive support from Diamphwe healthcare workers, which then improved the management of non-communicable diseases and patient care at Diamphwe. It has also shown how family medicine physicians can improve HC capacity through support and mentorship.

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