Waiting Time In a Multi-Server Cutoff-Priority Queue, and Its Application to an Urban Ambulance Service

1980 ◽  
Vol 28 (5) ◽  
pp. 1168-1188 ◽  
Author(s):  
I. D. S. Taylor ◽  
J. G. C. Templeton
Keyword(s):  
Waiting Time ◽  
Priority Queue ◽  
Ambulance Service ◽  
Multi Server

scholarly journals A Priority Queue with Many Customer Types, Correlated Arrivals and Changing Priorities

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1292
Author(s):  
Seokjun Lee ◽  
Sergei Dudin ◽  
Olga Dudina ◽  
Chesoong Kim ◽  
Valentina Klimenok
Keyword(s):  
Markov Chain ◽  
Waiting Time ◽  
Performance Indicators ◽  
Queueing System ◽  
Priority Queue ◽  
First Aid ◽  
Single Server ◽  
Finite Buffer ◽  
Impatient Customers ◽  
Arrival Times

A single-server queueing system with a finite buffer, several types of impatient customers, and non-preemptive priorities is analyzed. The initial priority of a customer can increase during its waiting time in the queue. The behavior of the system is described by a multi-dimensional Markov chain. The generator of this chain, having essential dependencies between the components, is derived and formulas for computation of the most important performance indicators of the system are presented. The dependence of some of these indicators on the capacity of the buffer space is illustrated. The profound effect of the phenomenon of correlation of successive inter-arrival times and variance of the service time is numerically demonstrated. Results can be used for the optimization of dispatching various types of customers in information transmission systems, emergency departments and first aid stations, perishable foods supply chains, etc.

scholarly journals Approximations for the waiting-time distribution in an $$M/PH/c$$ M / P H / c priority queue

OR Spectrum ◽  
2015 ◽  
Vol 37 (2) ◽  
pp. 529-552 ◽  
Author(s):  
A. Al Hanbali ◽  
E. M. Alvarez ◽  
M. C. van der Heijden
Keyword(s):  
Waiting Time ◽  
Time Distribution ◽  
Priority Queue ◽  
Waiting Time Distribution

Waiting-Time Percentiles in the Multi-server Mx/G/c Queue with Batch Arrivals

1987 ◽  
Vol 1 (1) ◽  
pp. 75-96 ◽  
Author(s):  
A. M. Eikeboom ◽  
H. C. Tijms
Keyword(s):  
Waiting Time ◽  
Time Distribution ◽  
Linear Interpolation ◽  
Service Time Distribution ◽  
Waiting Time Distribution ◽  
Batch Arrivals ◽  
Special Cases ◽  
Moment Approximation ◽  
Multi Server ◽  
Validation Experiments

This paper deals with the MX/G/c queue. Using analytical results for the special cases of the MX/M/c queue and the MX/D/c queue, a two-moment approximation is proposed for the waiting-time percentiles in the general case. This approximation is based on a linear interpolation with respect to the squared coefficient of variation of the service time distribution. Validation experiments indicate that this approximation performs quite well for practical purposes. In particular, the practically important percentiles in the tail of the waiting-time distribution are approximated extremely well.

AN ANALYSES ON PATIENTS’ QUEUING SYSTEM AT MUHAMMAD ABDULLAHI WASE SPECIALIST HOSPITAL, KANO

2021 ◽  
Vol 5 (2) ◽  
pp. 344-350
Author(s):  
Yunusa Ojirobe ◽  
Abubakar Yahaya ◽  
Muhammad Abdulkarim
Keyword(s):  
Waiting Time ◽  
Performance Indicators ◽  
Goodness Of Fit ◽  
Primary Data ◽  
Single Server ◽  
Utilization Factor ◽  
Average Waiting Time ◽  
Number Of Patients ◽  
Multi Server ◽  
System Average

A major cause for concern in hospitals is congestion, which brings about untoward hardship to patients due to long queues and delay in service delivery. This paper seeks to minimize the waiting time of patients by comparing the performance indicators of a single server and multi-server model at the Paediatrics Department of Muhammad Abdullahi Wase Specialist Hospital Kano (MAWSHK). In order to achieve this, primary data was obtained through direct observation which in turn is subjected to the test of goodness of fit to ascertain the distribution that best describes the data. The performance indicators comprising utilization factor, average number of patients in the queue, average number of patients in the system, average waiting time in queue and average waiting time in system for a single server and multi-server model were computed and analyzed respectively. Our findings indicate that the G/G/4 model performs better compared to the G/G/1 model as it minimizes the waiting time of patients

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