scholarly journals Minimal cost service rate in priority queuing models for emergency cases in hospitals

2018 ◽  
Vol 6 (2) ◽  
pp. 50
Author(s):  
Nse S. Udoh ◽  
Idorenyin A. Etukudo
Keyword(s):  
Waiting Time ◽  
Service Rate ◽  
Time Cost ◽  
Service Cost ◽  
Queuing Model ◽  
Total Cost ◽  
Lower Priority ◽  
Queuing Models ◽  
Optimum Cost ◽  
Expected Waiting Time

Performance measures and waiting time cost for higher priority patients with severe cases over lower priority patients with stable cases using preemptive priority queuing model were obtained. Also, a total expected waiting time cost per unit time for service and the expected service cost per unit time for priority queuing models: M/M/2: ∞/NPP and M/M/2: ∞/PP were respectively formulated and optimized to obtain optimum cost service rate that minimizes the total cost. The results were applied to obtain optimum service rate that minimizes the total cost of providing and waiting for service at the emergency consulting unit of hospital.    

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

Note: The Value and Cost of the Customer’s Waiting Time

2020 ◽  
Author(s):  
Rachel R. Chen ◽  
Subodha Kumar ◽  
Jaya Singhal ◽  
Kalyan Singhal
Keyword(s):  
Waiting Time ◽  
Queue Length ◽  
Relative Cost ◽  
Queuing Models ◽  
Underlying Causes ◽  
Two Measures ◽  
Expected Waiting Time ◽  
The Cost ◽  
Shed Light ◽  
Key Parameter

The (relative) cost of the customer’s waiting time has long been used as a key parameter in queuing models, but it can be difficult to estimate. A recent study introduced a new queue characteristic, the value of the customer’s waiting time, which measures how an increase in the total customer waiting time reduces the servers’ idle time. This paper connects and contrasts these two fundamental concepts in the queuing literature. In particular, we show that the value can be equal to the cost of waiting when the queue is operated at optimal. In this case, we can use the observed queue length to compute the value of waiting, which helps infer the cost of waiting. Nevertheless, these two measures have very different economic interpretations, and in general, they are not equal. For nonoptimal queues, comparing the value with the cost helps shed light on the underlying causes of the customer’s waiting. Although it is tempting to conclude that customers in a queue with a lower value of waiting expect to wait longer, we find that the value of waiting in general does not have a monotonic relationship with the expected waiting time, nor with the expected queue length.

Development of a Heuristics Model for Simplifying a Multi-Channel Queuing System

2011 ◽  
Vol 367 ◽  
pp. 647-652
Author(s):  
B. Kareem ◽  
A. A. Aderoba
Keyword(s):  
Waiting Time ◽  
Channel Model ◽  
Single Channel ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Queuing Models ◽  
Significant Difference ◽  
Number Of Customers ◽  
Proportionality Principle

Queuing model has been discussed widely in literature. The structures of queuing systems are broadly divided into three namely; single, multi-channel, and mixed. Equations for solving these queuing problems vary in complexity. The most complex of them is the multi-channel queuing problem. A heuristically simplified equation based on relative comparison, using proportionality principle, of the measured effectiveness from the single and multi-channel models seems promising in solving this complex problem. In this study, six different queuing models were used from which five of them are single-channel systems while the balance is multi-channel. Equations for solving these models were identified based on their properties. Queuing models’ performance parameters were measured using relative proportionality principle from which complexity of multi-channel system was transformed to a simple linear relation of the form = . This showed that the performance obtained from single channel model has a linear relationship with corresponding to multi-channel, and is a factor which varies with the structure of queuing system. The model was tested with practical data collected on the arrival and departure of customers from a cocoa processing factory. The performances obtained based on average number of customers on line , average number of customers in the system , average waiting time in line and average waiting time in the system, under certain conditions showed no significant difference between using heuristics and analytical models.

scholarly journals Determination of Optimal Number of Servers at Network Queuing Nodes to Reduce Waiting Time in a Tertiary Institution Clinic in Bida, Nigeria

2020 ◽  
Vol 24 (9) ◽  
pp. 1631-1639
Author(s):  
I. Muhammad ◽  
L. Adamu
Keyword(s):  
Waiting Time ◽  
Current System ◽  
Optimal Number ◽  
Queuing System ◽  
Tertiary Institution ◽  
Queuing Model ◽  
Direct Observations ◽  
Expected Waiting Time ◽  
System Nodes

In this paper, a network queuing model that determines optimal numbers of servers at the nodes of the school clinic network queuing system to  reduce waiting time of the patients has been presented. The relevant data was collected for a period four weeks, through direct observations and interviews. The number of arrivals and departures were also obtained. The total expected waiting time of the patient in the current system before modification was 50minutes with total number of 10 servers in all the nodes, while the total new expected waiting time of patient in the system after modification was reduced to 19 minutes with total number of 17 servers in all the nodes. The study has determined optimal number of servers at the nodes of the school clinic network system. Results from this study is an important information to the management of the school clinic for proper planning and better service delivery. Keywords: Network Queuing System, Nodes, Servers, School Clinic.

scholarly journals Mathematical analysis of single queue multi server and multi queue multi server queuing models: comparison study

2015 ◽  
Vol 3 (3) ◽  
pp. 97 ◽  
Author(s):  
Vijay Prasad ◽  
Badshah V.H ◽  
Tariq Ahmad Koka
Keyword(s):  
Mathematical Analysis ◽  
Comparison Study ◽  
Queuing Model ◽  
Total Cost ◽  
Queuing Models ◽  
Models Comparison ◽  
Mathematical Equations ◽  
Expected Total Cost ◽  
Multi Server ◽  
Better Than

<p>In the research paper entitled Mathematical Analysis of Single Queue Multi Server and Multi Queue Multi Server Queuing Model, Prasad and Badshah [7] were proved that single queue multi server model is better than multi queue multi server model, and discussed the relation between the performance measures of these two models, and derive the mathematical equations. In this paper we derive the total cost with assumption of certain Waiting cost in both cases. Also, prove that the expected total cost is less for single queue multi server model as comparing with multi queue multi server model.</p>

scholarly journals A Study of Service in Restaurant by Using Queuing Model

2013 ◽  
Vol 5 ◽  
pp. 14-18
Author(s):  
G.D. Mishra ◽  
Vijiya Singh Chauhan ◽  
Nikita Chandra
Keyword(s):  
Numerical Model ◽  
Waiting Time ◽  
Queuing Theory ◽  
Arrival Rate ◽  
Utilization Rate ◽  
Service Rate ◽  
Case Scenario ◽  
Queuing Model ◽  
Restaurant Management ◽  
Potential Customers

The restaurants want to avoid losing their customers due to a long wait on the line. This shows a need of a numerical model for the restaurant management to understand the situation better. This paper aims to show that queuing theory satisfies the model when tested with a real-case scenario. We obtained the data from a restaurant. We then derive the arrival rate, service rate, utilization rate, waiting time in queue and the probability of potential customers to balk based on the data using Little’s Theorem and M/M/1 queuing model. We conclude the paper by discussing the benefits of performing queuing analysis to a busy restaurant.

scholarly journals Confidence limits for expected waiting time of two queuing models

ORiON ◽  
2004 ◽  
Vol 20 (1) ◽  
Author(s):  
VSS Yadavalli ◽  
K Adendorff ◽  
G Erasmus ◽  
P Chandrasekhar ◽  
SP Deepa
Keyword(s):  
Waiting Time ◽  
Confidence Limits ◽  
Queuing Models ◽  
Expected Waiting Time

Studying the appointment scheduling window considering patient no-show behavior in one public and one private outpatient clinics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohsen Abdoli ◽  
Mostafa Zandieh ◽  
Sajjad Shokouhyar
Keyword(s):  
Waiting Time ◽  
Queuing System ◽  
System Capacity ◽  
Outpatient Clinics ◽  
Effective Parameters ◽  
Queuing Model ◽  
Public And Private ◽  
Content Type ◽  
Total Cost ◽  
Patient Waiting Time

Purpose This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized. Design/methodology/approach In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model. Findings In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment. Originality/value Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

An QPSL Queuing Model for Load Balancing in Cloud Computing

2020 ◽  
Vol 16 (3) ◽  
pp. 33-48
Author(s):  
Shadab Siddiqui ◽  
Manuj Darbari ◽  
Diwakar Yagyasen
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Queuing Theory ◽  
Service Rate ◽  
Queuing Model ◽  
Queuing Models ◽  
Fifo Queue ◽  
Queue Discipline ◽  
Service Rates ◽  
Waiting Lines

Load balancing is the process of distributing a workload among various servers. Queuing is the most common scenario for day-to-day applications. Queuing theory is used to study the problem of waiting lines. Queuing theory bridges the gap between service demands and the delay in replies given to users. The proposed QPSL Queuing Model makes use of M/M/k queue with FIFO queue discipline for load balancing in cloud computing. The model makes use of exponential distribution for calculating service rates and Poisson distribution for calculating waiting lines. The proposed QPSL queuing model is also compared with other existing queuing models for load balancing on various parameters. The experimental analysis depicts that QPSL model performed better in terms of service rate and response time.

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