The actual waiting time of each customer in a GI/G/1 queue

1979 ◽  
Vol 16 (04) ◽  
pp. 910-916
Author(s):  
Do Le Minh
Keyword(s):  
Waiting Time ◽  
Queueing System ◽  
Random Set ◽  
Arrival Times ◽  
Recursive Scheme ◽  
Set Up

This paper studies a generalization of theGI/G/1queueing system in which the inter-arrival times are not necessarily identically distributed and there is a random set-up time for customers who arrive when the server is idle. A recursive scheme is derived to obtain the distribution of the actual waiting time of each customer in the system.

The actual waiting time of each customer in a GI/G/1 queue

1979 ◽  
Vol 16 (4) ◽  
pp. 910-916
Author(s):  
Do Le Minh
Keyword(s):  
Waiting Time ◽  
Queueing System ◽  
Random Set ◽  
Arrival Times ◽  
Recursive Scheme ◽  
Set Up

This paper studies a generalization of the GI/G/1 queueing system in which the inter-arrival times are not necessarily identically distributed and there is a random set-up time for customers who arrive when the server is idle. A recursive scheme is derived to obtain the distribution of the actual waiting time of each customer in the system.

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.

On the waiting time distribution in a generalized GI/G/1 queueing system

1971 ◽  
Vol 8 (2) ◽  
pp. 241-251 ◽  
Author(s):  
Jacqueline Loris-Teghem
Keyword(s):  
Waiting Time ◽  
Service Time ◽  
Queueing System ◽  
Random Variables ◽  
Markovian Process ◽  
Random Time ◽  
Arbitrary Distribution ◽  
Waiting Time Distribution ◽  
Transient Behaviour ◽  
Set Up

The model considered in this paper describes a queueing system in which the station is dismantled at the end of a busy period and re-established on arrival of a new customer, in such a way that the closing-down process consists of N1 phases of random duration and that a customer 𝒞n who arrives while the station is being closed down must wait a random time idn(i = 1, ···, N1) if the ith phase is going on at the arrival instant. (For each fixed index i, the random variables idn are identically distributed.) A customer 𝒞n arriving when the closing-down of the station is already accomplished has to wait a random time (N1 + 1)dn corresponding to the set up time of the station. Besides, a customer 𝒞n who arrives when the station is busy has to wait an additional random time 0dn. We thus have (N1 + 2) types of “delay” (additional waiting time). Similarly, we consider (N2 + 2) types of service time and (N3 + 2) probabilities of joining the queue. This may be formulated as a model with (N + 2) types of triplets (delay, service time, probability of joining the queue). We consider the general case where the random variables defining the model all have an arbitrary distribution.The process {wn}, where wn denotes the waiting time of customer 𝒞n if he joins the queue at all, is not necessarily Markovian, so that we first study (by algebraic considerations) the transient behaviour of a Markovian process {vn} related to {wn}, and then derive the distribution of the variables wn.

On the waiting time distribution in a generalized GI/G/1 queueing system

1971 ◽  
Vol 8 (02) ◽  
pp. 241-251 ◽  
Author(s):  
Jacqueline Loris-Teghem
Keyword(s):  
Waiting Time ◽  
Service Time ◽  
Queueing System ◽  
Random Variables ◽  
Markovian Process ◽  
Random Time ◽  
Arbitrary Distribution ◽  
Waiting Time Distribution ◽  
Transient Behaviour ◽  
Set Up

The model considered in this paper describes a queueing system in which the station is dismantled at the end of a busy period and re-established on arrival of a new customer, in such a way that the closing-down process consists of N 1 phases of random duration and that a customer 𝒞 n who arrives while the station is being closed down must wait a random time idn (i = 1, ···, N 1) if the ith phase is going on at the arrival instant. (For each fixed index i, the random variables idn are identically distributed.) A customer 𝒞 n arriving when the closing-down of the station is already accomplished has to wait a random time (N 1 + 1) dn corresponding to the set up time of the station. Besides, a customer 𝒞 n who arrives when the station is busy has to wait an additional random time 0 dn. We thus have (N 1 + 2) types of “delay” (additional waiting time). Similarly, we consider (N 2 + 2) types of service time and (N 3 + 2) probabilities of joining the queue. This may be formulated as a model with (N + 2) types of triplets (delay, service time, probability of joining the queue). We consider the general case where the random variables defining the model all have an arbitrary distribution. The process {wn }, where wn denotes the waiting time of customer 𝒞 n if he joins the queue at all, is not necessarily Markovian, so that we first study (by algebraic considerations) the transient behaviour of a Markovian process {vn } related to {wn }, and then derive the distribution of the variables wn.

Feeder Bus Timetable Design and Vehicle Size Setting in Peak Hour Demand Conditions

2019 ◽  
Vol 2673 (10) ◽  
pp. 321-332 ◽  
Author(s):  
Xueping Dou ◽  
Qiang Meng
Keyword(s):  
Mixed Integer ◽  
Arrival Times ◽  
Non Linear Programming ◽  
Bee Colony ◽  
Proposed Model ◽  
Feeder Bus ◽  
Minlp Model ◽  
Failure Cost ◽  
Set Up ◽  
The Given

This study proposes a solution to the feeder bus timetabling problem, in which the terminal departure times and vehicle sizes are simultaneously determined based on the given transfer passengers and their arrival times at a bus terminal. The problem is formulated as a mixed integer non-linear programming (MINLP) model with the objective of minimizing the transfer waiting time of served passengers, the transfer failure cost of non-served passengers, and the operating costs of bus companies. In addition to train passengers who plan to transfer to buses, local passengers who intend to board buses are considered and treated as passengers from virtual trains in the proposed model. Passenger attitudes and behaviors toward the waiting queue caused by bus capacity constraints in peak hour demand conditions are explicitly embedded in the MINLP model. A hybrid artificial bee colony (ABC) algorithm is developed to solve the MINLP model. Various experiments are set up to account for the performance of the proposed model and solution algorithm.

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.

On the waiting time distribution in a generalized queueing system with uniformly bounded sojourn times

1972 ◽  
Vol 9 (3) ◽  
pp. 642-649 ◽  
Author(s):  
Jacqueline Loris-Teghem
Keyword(s):  
Generating Function ◽  
Waiting Time ◽  
Service Time ◽  
Time Distribution ◽  
Queueing System ◽  
Waiting Time Distribution ◽  
Stieltjes Transform ◽  
Sojourn Times ◽  
Stieltjes Transforms ◽  
Uniformly Bounded

A generalized queueing system with (N + 2) types of triplets (delay, service time, probability of joining the queue) and with uniformly bounded sojourn times is considered. An expression for the generating function of the Laplace-Stieltjes transforms of the waiting time distributions is derived analytically, in a case where some of the random variables defining the model have a rational Laplace-Stieltjes transform.The standard Kl/Km/1 queueing system with uniformly bounded sojourn times is considered in particular.

On the comparison of waiting times in tandem queues

1981 ◽  
Vol 18 (03) ◽  
pp. 707-714 ◽  
Author(s):  
Shun-Chen Niu
Keyword(s):  
Waiting Time ◽  
Queueing System ◽  
Waiting Times ◽  
Distribution Functions ◽  
Partial Ordering ◽  
Tandem Queues ◽  
Order Relations ◽  
Service Distribution ◽  
In Series ◽  
Definition Of

Using a definition of partial ordering of distribution functions, it is proven that for a tandem queueing system with many stations in series, where each station can have either one server with an arbitrary service distribution or a number of constant servers in parallel, the expected total waiting time in system of every customer decreases as the interarrival and service distributions becomes smaller with respect to that ordering. Some stronger conclusions are also given under stronger order relations. Using these results, bounds for the expected total waiting time in system are then readily obtained for wide classes of tandem queues.

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