scholarly journals An Improved Queuing Model for Reducing Average Waiting Time of Emergency Surgical Patients Using Preemptive Priority Scheduling

2021 ◽  
Vol 9 (6) ◽  
pp. 827-840
Keyword(s):  
Waiting Time ◽  
Time Management ◽  
Arrival Rate ◽  
Preemptive Priority ◽  
Queuing Model ◽  
Priority Scheduling ◽  
Average Waiting Time ◽  
Public Sector Hospital ◽  
Medical Facilities ◽  
The Mean

Our research objective is to reduce the Average Waiting Time for patients in an Emergency Department of public sector hospital. We have based our model on M/M/s Queuing System, our study revealssignificant findings on arrival rate of patients. During this simulation, we have used a preemptive priority scheduling model. In our practice, the arrival rate followed a Poisson distribution, averaging 30 patients per hour, with the Mean Service time of1.5 hours and Average Waiting Time recorded around 12.13 minutes. This research offersvaluable help to achieve better time management in emergency departments of high-density medical facilities.

scholarly journals Deskripsi Sistem Antrian pada Klinik Dokter Spesialis Penyakit Dalam

2012 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Deiby T Salaki
Keyword(s):  
Direct Observation ◽  
Waiting Time ◽  
Arrival Rate ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Number Of Patients

DESKRIPSI SISTEM ANTRIAN PADA KLINIK DOKTER SPESIALIS PENYAKIT DALAM ABSTRAK Penelitian ini dilakukan untuk mengetahui deskripsi sistem antrian pada klinik dokter internist. Pengumpulan data dilakukan secara langsung pada klinik dokter internist JHA selama 12 hari, selama 2 jam waktu pengamatan tiap harinya pada periode sibuk.. Model antrian yang digunakan adalah model (M/M/1) : (FIFO/~/~), tingkat kedatangan bersebaran poisson, waktu pelayanan bersebaran eksponensial, dengan jumlah pelayanan adalah seorang dokter, disiplin antrian yang digunakan adalah pasien yang pertama datang yang pertama dilayani, jumlah pelayanan dalam sistem dan ukuran populasi pada sumber masukan adalah tak berhingga.  Sistem antrian pada klinik ini memiliki kecepatan kedatangan pelayanan anamnesa rata-rata  menit 1 orang pasien datang, kecepatan kedatangan pelayanan pemeriksaan fisik rata-rata  menit 1 orang pasien datang, rata-rata waktu pelayanan anamnesa untuk  seorang pasien  menit, rata-rata waktu pelayanan pemeriksaan fisik untuk  seorang pasien  menit, peluang kesibukan  pelayanan anamnesa sebesar , peluang kesibukan  pelayanan pemeriksaan fisik sebesar , dan peluang pelayanan anamnesa menganggur sebesar , peluang pelayanan pemeriksaan fisik menganggur sebesar . Rata-rata banyaknya pengantri untuk anamnesa adalah  pasien sedangkan untuk pemeriksaan fisik  pasien, rata-rata banyaknya pengantri dalam sistem adalah  pasien, waktu rata-rata seorang pasien dalam klinik adalah  menit, waktu rata-rata seseorang pasien untuk antri adalah  menit. Kata kunci: Sistem Antrian, Klinik Penyakit Dalam  DESCRIPTION OF QUEUING SYSTEM AT THE INTERNIST CLINIC ABSTRACT This research determines the description of queuing system at the internist Clinic. Data collected by direct observation during 12 days and in 2 hours. Queuing model that used is model of (M/M/1): (FIFO /~/~). Based on the research, the clinic has 3.256 minutes per patient in average arrival rate for anamnesys, the average arrival rate for diaagnosys is 3.255 minutes per patient, average service speed for anamnesys is 2.675 minutes per patient, average service speed for diagnosys is 12.635 minutes, the probability of busy periods for anamnesys is 0.864, the probability of busy periods for diagnosys is 0.832 and probability of all free services or no patient in the anamnesys equal to 0.136, probability of all free services or no patient in the anamnesys equal to 0.168. The average number of patients in anamnesys queue is 5 patients, the average number of patients in diagnosys queue is 4 patients, the average number of patients in the system is 10 patients, the average waiting time in the system is 47.078 minutes and the average queuing time is 31.660 minutes. Keywords: Queuing system, internist clinic

scholarly journals Super Smart Optimized Round Robin

2016 ◽  
Vol 2 (2) ◽  
pp. 19-21
Author(s):  
Achmad Teguh Wibowo
Keyword(s):  
Waiting Time ◽  
Turnaround Time ◽  
Round Robin ◽  
Priority Scheduling ◽  
Average Waiting Time ◽  
First Come First Serve ◽  
Time Average ◽  
Average Turnaround Time

Aspek penting dalam sistem operasi adalah multiprogramming. Multiprogramming adalah proses atau metode yang digunakan untuk mengekssekusi beberapa proses secara bersamaan dalam memori. Tujuan utamanya adalah untuk meminimalkan Average Waiting Time, Average Turnaround Time, dan memaksimalkan penggunaan CPU. Ada berbagai algoritma yang digunakan dalam multiprogramming seperti First Come First Serve (FCFS), Shortest Job First (SJF), Priority Scheduling (PS) dan Round Robin(RR). Diantara semua itu yang paling sering digunakan adalah Round Robin. Round Robin merupakan algoritma penjadwalan yang optimal dengn sistem timeshared. Dalam RR, waktu kuantum bersifat statis dan algoritma ini bergantung pada besarnya kuantum yang dipilih/digunakan. Kuantum inilah yang berpengaruh pada Average Waiting Time dan Average Turnaround Time nantinya. Tujuan dari makalah ini adalah mengusulkan algoritma yang lebih baik daripada Round Robin sederhana dan Smart Optimized Round Robin sebelumnya.

The Study on Production Performances of Part Transporter in Flexible Manufacturing System by Using Simulation Method

2012 ◽  
Vol 576 ◽  
pp. 714-717
Author(s):  
Mohammad Iqbal ◽  
Muhammad Ridwan Andi Purnomo ◽  
Muhammad Ammar Bin Mohd Imra ◽  
Mohamed Konneh ◽  
A.N. Mustafizul Karim
Keyword(s):  
Waiting Time ◽  
Flexible Manufacturing ◽  
Material Handling ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Arrival Rate ◽  
Simulation Method ◽  
Simulation Software ◽  
Average Waiting Time ◽  
Processing Times

Material handling is one of major components in Flexible Manufacturing System (FMS). Any improvement of material handling capability is to affect the performance of the whole system. This paper discusses the simulation study on the effect of part arrival rate and dispatching rules to the average waiting time and production rate of the FMS. The facilities of the system were modeled into simulation environment by using Arena Simulation Software. The production parameters such as machine processing times, part transportation speed and type of products were put into the model to represent the behaviors of the real system. Two rules have been considered in the study, i. e. first come first served (FCFS), and shortest processing time (SPT). Average waiting time and productivity were taken into account as performance measures of the system. The result of the study showed that SPT rule gives shorter average waiting time and higher productivity. Based on this result, the SPT rules would be used to control part transporter in order to have a better performance of the FMS.

scholarly journals Optimization Design of X-ray Conveyer Belt Length for Subway Security Check Systems in Beijing, China

2020 ◽  
Vol 12 (5) ◽  
pp. 2133 ◽  
Author(s):  
Zhonghua Wei ◽  
Sinan Chu ◽  
Zhengde Huang ◽  
Shi Qiu ◽  
Qixuan Zhao
Keyword(s):  
Waiting Time ◽  
Optimization Design ◽  
Arrival Rate ◽  
Conveyer Belt ◽  
Modeling Framework ◽  
Queuing Model ◽  
X Ray ◽  
Potential Safety ◽  
Subway Stations ◽  
Security Check

The frequent terrorist attacks in subways has dramatically increased the necessity and importance of security check systems (SCSs). The implementation of a SCS in China has successfully eliminated lots of potential safety hazards. However, the excessive waiting time due to the SCS is also an issue. SCS efficiency is greatly affected by the length of the conveyer belt of the X-ray machine (CBXM). A scheme for optimizing the CBXM length to accommodate different passenger flows is proposed in this paper. A modeling framework is developed for associating the CBXM length with the queuing waiting time based on a M/M/1/N queuing model. The optimal scheme of CBXM length calculated from the model demonstrates that the passenger queuing time is saved by 15.7%, 16.0%, and 23.3% with the passenger arrival rate of 4000, 5000, and 6000, respectively, greatly reducing queuing crowdedness. The scheme can be used to select X-ray machines for subway stations by their passenger arrival rates. In addition, the findings of this paper could be a crucial supplement and perfect the design code of subway SCSs.

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.

A queue with Markov-dependent service times

1968 ◽  
Vol 5 (02) ◽  
pp. 461-466
Author(s):  
Gerold Pestalozzi
Keyword(s):  
Steady State ◽  
Generating Function ◽  
Waiting Time ◽  
Service Time ◽  
Queueing System ◽  
Single Channel ◽  
Probability Generating Function ◽  
Average Waiting Time ◽  
Poisson Input ◽  
The Mean

A queueing system is considered where each item has a property associated with it, and where the service time interposed between two items depends on the properties of both of these items. The steady state of a single-channel queue of this type, with Poisson input, is investigated. It is shown how the probability generating function of the number of items waiting can be found. Easily applied approximations are given for the mean number of items waiting and for the average waiting time.

scholarly journals Mean Waiting Time in Large-Scale and Critically Loaded Power of d Load Balancing Systems

2021 ◽  
Vol 5 (2) ◽  
pp. 1-34
Author(s):  
Tim Hellemans ◽  
Benny Van Houdt
Keyword(s):  
Load Balancing ◽  
Waiting Time ◽  
Large Scale ◽  
Mean Field ◽  
Arrival Rate ◽  
Mean Field Limit ◽  
Mean Waiting Time ◽  
Constant Rate ◽  
Finite Set ◽  
The Mean

Mean field models are a popular tool used to analyse load balancing policies. In some exceptional cases the waiting time distribution of the mean field limit has an explicit form. In other cases it can be computed as the solution of a set of differential equations. In this paper we study the limit of the mean waiting time E[Wλ] as the arrival rate λ approaches 1 for a number of load balancing policies in a large-scale system of homogeneous servers which finish work at a constant rate equal to one and exponential job sizes with mean 1 (i.e. when the system gets close to instability). As E[Wλ] diverges to infinity, we scale with -log(1-λ) and present a method to compute the limit limλ-> 1- -E[Wλ]/l(1-λ). We show that this limit has a surprisingly simple form for the load balancing algorithms considered. More specifically, we present a general result that holds for any policy for which the associated differential equation satisfies a list of assumptions. For the well-known LL(d) policy which assigns an incoming job to a server with the least work left among d randomly selected servers these assumptions are trivially verified. For this policy we prove the limit is given by 1/d-1. We further show that the LL(d,K) policy, which assigns batches of K jobs to the K least loaded servers among d randomly selected servers, satisfies the assumptions and the limit is equal to K/d-K. For a policy which applies LL(di) with probability pi, we show that the limit is given by 1/ ∑i pi di - 1. We further indicate that our main result can also be used for load balancers with redundancy or memory. In addition, we propose an alternate scaling -l(pλ) instead of -l(1-λ), where pλ is adapted to the policy at hand, such that limλ-> 1- -E[Wλ]/l(1-λ)=limλ-> 1- -E[Wλ]/l(pλ), where the limit limλ-> 0+ -E[Wλ]/l(pλ) is well defined and non-zero (contrary to limλ-> 0+ -E[Wλ]/l(1-λ)). This allows to obtain relatively flat curves for -E[Wλ]/l(pλ) for λ ∈ [0,1] which indicates that the low and high load limits can be used as an approximation when λ is close to one or zero. Our results rely on the earlier proven ansatz which asserts that for certain load balancing policies the workload distribution of any finite set of queues becomes independent of one another as the number of servers tends to infinity.

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 ACEA: A Queueing Model-Based Elastic Scaling Algorithm for Container Cluster

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kui Li ◽  
Yi-mu Ji ◽  
Shang-dong Liu ◽  
Hai-chang Yao ◽  
Hang Li ◽  
...  
Keyword(s):  
Resource Utilization ◽  
Waiting Time ◽  
Sudden Change ◽  
Solution Space ◽  
Performance Model ◽  
Utilization Rate ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Elastic Scaling ◽  
The Relationship

Elastic scaling is one of the techniques to deal with the sudden change of the number of tasks and the long average waiting time of tasks in the container cluster. The unreasonable resource supply may lead to the low comprehensive resource utilization rate of the cluster. Therefore, balancing the relationship between the average waiting time of tasks and the comprehensive resource utilization rate of the cluster based on the number of tasks is the key to elastic scaling. In this paper, an adaptive scaling algorithm based on the queuing model called ACEA is proposed. This algorithm uses the hybrid multiserver queuing model (M/M/s/K) to quantitatively describe the relationship among number of tasks, average waiting time of tasks, and comprehensive resource utilization rate of cluster and builds the cluster performance model, evaluation function, and quality of service (QoS) constraints. Particle swarm optimization (PSO) is used to search feasible solution space determined by the constraint relation of ACEA quickly, so as to improve the dynamic optimization performance and convergence timeliness of ACEA. The experimental results show that the algorithm can ensure the comprehensive resource utilization rate of the cluster while the average waiting time of tasks meets the requirement.

scholarly journals Convoy Effect Elimination in FCFS Scheduling

2020 ◽  
Vol 9 (3) ◽  
pp. 3218-3221
Keyword(s):  
Operating Systems ◽  
Waiting Time ◽  
Turnaround Time ◽  
Round Robin ◽  
Process Scheduling ◽  
Priority Scheduling ◽  
Average Waiting Time ◽  
Novel Method ◽  
Lengthy Process

One of the important activities of operating systems is process scheduling. There are many algorithms available for scheduling like First Come First Served, Shortest Job First, Priority Scheduling and Round Robin. The fundamental algorithm is First Come First Served. It has some drawback of convoy effect. Convoy effect occurs when the small processes are waiting for lengthy process to complete. In this paper novel method is proposed to reduce convoy effect and to make the Scheduling optimal which reduces average waiting time and turnaround time.

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