A quantitative continuity theorem for the mean stationary waiting time in GI/GI/1

Optimization ◽  
1976 ◽  
Vol 7 (4) ◽  
pp. 595-599
Author(s):  
M. Kotzturek ◽  
D. Stoyan
Keyword(s):  
Waiting Time ◽  
Continuity Theorem ◽  
The Mean ◽  
Stationary Waiting Time

SET-VALUED PERFORMANCE APPROXIMATIONS FOR THE QUEUE GIVEN PARTIAL INFORMATION

2020 ◽  
pp. 1-23
Author(s):  
Yan Chen ◽  
Ward Whitt
Keyword(s):  
Steady State ◽  
Decay Rate ◽  
Waiting Time ◽  
Partial Information ◽  
Upper And Lower Bounds ◽  
Interarrival Time ◽  
Limited Information ◽  
Wide Range ◽  
The Mean ◽  
Third Moment

In order to understand queueing performance given only partial information about the model, we propose determining intervals of likely values of performance measures given that limited information. We illustrate this approach for the mean steady-state waiting time in the $GI/GI/K$ queue. We start by specifying the first two moments of the interarrival-time and service-time distributions, and then consider additional information about these underlying distributions, in particular, a third moment and a Laplace transform value. As a theoretical basis, we apply extremal models yielding tight upper and lower bounds on the asymptotic decay rate of the steady-state waiting-time tail probability. We illustrate by constructing the theoretically justified intervals of values for the decay rate and the associated heuristically determined interval of values for the mean waiting times. Without extra information, the extremal models involve two-point distributions, which yield a wide range for the mean. Adding constraints on the third moment and a transform value produces three-point extremal distributions, which significantly reduce the range, producing practical levels of accuracy.

scholarly journals SIMULASI WAKTU TUNGGU MAHASISWA DAN DOSEN DI LAYANAN AKADEMIK UNIVERSITAS BUNDA MULIA

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Rudy Santosa Sudirga
Keyword(s):  
Response Time ◽  
Waiting Time ◽  
Operations Management ◽  
Heavy Traffic ◽  
Simulation Models ◽  
Operation Performance ◽  
Service Operation ◽  
Mean And Variance ◽  
The Mean ◽  
Time In Service

<p>The Management of Academic Service continues to be a major challenge for many college, high school and college organizations in providing better services with fewer resources. The allocation of service staffs and response-time in service involve many challenging issues, because the mean and variance of the response-time in service can be increased dramatically with the intensity of heavy traffic. This study discusses how to use simulation models to improve response time in service operation. Performance at the Academic Service as a whole can be considered very good and is still idle due to utilization of Academic Service, which is still equal to an average of 17%, or it can be said that the workload is not too excessive and deemed to be able to serve the students and lecturers. The performance of Academic Sevice University Bunda Mulia can be considered excellent in terms of operations management, as indicated by the average waiting time, which is very short at only 9.10 seconds.<br />Keywords: Queueing System, Waiting Time, and Simulation</p>

scholarly journals Assessment of patients waiting and service times in the ophthalmology clinic of a public tertiary hospital in Nigeria

2020 ◽  
Vol 54 (4) ◽  
pp. 231-237
Author(s):  
Lateefat B. Olokoba ◽  
Kabir A. Durowade ◽  
Feyi G. Adepoju ◽  
Abdulfatai B. Olokoba
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Sampling Technique ◽  
Tertiary Hospital ◽  
Cross Sectional Study ◽  
Ethical Review ◽  
Cross Sectional ◽  
Arrival Times ◽  
The Mean ◽  
Service Times

Introduction: Long waiting time in the out-patient clinic is a major cause of dissatisfaction in Eye care services. This study aimed to assess patients’ waiting and service times in the out-patient Ophthalmology clinic of UITH. Methods: This was a descriptive cross-sectional study conducted in March and April 2019. A multi-staged sampling technique was used. A timing chart was used to record the time in and out of each service station. An experience based exit survey form was used to assess patients’ experience at the clinic. The frequency and mean of variables were generated. Student t-test and Pearson’s correlation were used to establish the association and relationship between the total clinic, service, waiting, and clinic arrival times. Ethical approval was granted by the Ethical Review Board of the UITH. Result: Two hundred and twenty-six patients were sampled. The mean total waiting time was 180.3± 84.3 minutes, while the mean total service time was 63.3±52.0 minutes. Patient’s average total clinic time was 243.7±93.6 minutes. Patients’ total clinic time was determined by the patients’ clinic status and clinic arrival time. Majority of the patients (46.5%) described the time spent in the clinic as long but more than half (53.0%) expressed satisfaction at the total time spent at the clinic. Conclusion: Patients’ clinic and waiting times were long, however, patients expressed satisfaction with the clinic times.

scholarly journals Decomposition of M/M/1 With Unreliable Service and a Working Vacation

2020 ◽  
Vol 9 (1) ◽  
pp. 63
Author(s):  
Joshua Patterson ◽  
Andrzej Korzeniowski
Keyword(s):  
Closed Form ◽  
Failure Rate ◽  
Waiting Time ◽  
Queue Length ◽  
Service Failure ◽  
Working Vacation ◽  
Stationary Queue Length ◽  
Stationary Waiting Time ◽  
Relationship Of ◽  
The Relationship

We use the stationary distribution for the M/M/1 with Unreliable Service and aWorking Vacation (M/M/1/US/WV) given explicitly in (Patterson &amp; Korzeniowski, 2019) to find a decomposition of the stationary queue length N. By applying the distributional form of Little&#39;s Law the Laplace-tieltjes Transform of the stationary customer waiting time W is derived. The closed form of the expected value and variance for both N and W is found and the relationship of the expected stationary waiting time as a function of the service failure rate is determined.

Another martingale bound on the waiting-time distribution in GI/G/1 queues

1979 ◽  
Vol 16 (2) ◽  
pp. 454-457 ◽  
Author(s):  
Harry H. Tan
Keyword(s):  
Waiting Time ◽  
Upper Bound ◽  
Time Distribution ◽  
Waiting Time Distribution ◽  
Martingale Approach ◽  
Stationary Waiting Time

A new upper bound on the stationary waiting-time distribution of a GI/G/1 queue is derived following Kingman's martingale approach. This bound is generally stronger than Kingman's upper bound and is sometimes stronger than an upper bound derived by Ross.

scholarly journals Estimation of the mean waiting time of a customer subject to balking: a simulation study

2007 ◽  
Vol 19 (1) ◽  
pp. 63-63
Author(s):  
Jaejin Jang ◽  
Jaewoo Chung ◽  
Jungdae Suh ◽  
Jongtae Rhee
Keyword(s):  
Waiting Time ◽  
Simulation Study ◽  
Mean Waiting Time ◽  
The Mean

Stationary waiting-time distributions in the GI/PH/1 queue

1981 ◽  
Vol 18 (4) ◽  
pp. 901-912 ◽  
Author(s):  
Marcel F. Neuts
Keyword(s):  
Waiting Time ◽  
Embedded Markov Chain ◽  
Asymptotic Results ◽  
Waiting Time Distributions ◽  
Structured Systems ◽  
Queue Discipline ◽  
The Matrix ◽  
Constant Coefficients ◽  
Linear Differential ◽  
Stationary Waiting Time

It is known that the stable GI/PH/1 queue has an embedded Markov chain whose invariant probability vector is matrix-geometric with a rate matrix R. In terms of the matrix R, the stationary waiting-time distributions at arrivals, at an arbitrary time point and until the customer's departure may be evaluated by solving finite, highly structured systems of linear differential equations with constant coefficients. Asymptotic results, useful in truncating the computations, are also obtained. The queue discipline is first-come, first-served.

scholarly journals Waiting Time Problems for Patterns in a Sequence of Multi-State Trials

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1893
Author(s):  
Bara Kim ◽  
Jeongsim Kim ◽  
Jerim Kim
Keyword(s):  
Waiting Time ◽  
Time Distribution ◽  
Probability Generating Function ◽  
Finite Collection ◽  
Numerical Inversion ◽  
Analytic Method ◽  
Waiting Time Distribution ◽  
Mean Waiting Time ◽  
The Matrix ◽  
The Mean

In this paper, we investigate waiting time problems for a finite collection of patterns in a sequence of independent multi-state trials. By constructing a finite GI/M/1-type Markov chain with a disaster and then using the matrix analytic method, we can obtain the probability generating function of the waiting time. From this, we can obtain the stopping probabilities and the mean waiting time, but it also enables us to compute the waiting time distribution by a numerical inversion.

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