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.

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 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 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.

A Study to Setting Function of Queuing Discipline in Highway Tollgate

2013 ◽  
Vol 734-737 ◽  
pp. 1594-1597 ◽  
Author(s):  
Li Ying Wang ◽  
Yong Feng
Keyword(s):  
Waiting Time ◽  
Road Traffic ◽  
Hypothesis Test ◽  
Arrival Rate ◽  
Highway Traffic ◽  
Contributing Factors ◽  
Queuing Model ◽  
Traffic Efficiency ◽  
Traffic Jams ◽  
Traffic Capacity

The traffic capacity of the highway toll station strongly affects the traffic capacity of the whole road, and restricts road traffic. In order to avoid the highway traffic jams and decline of traffic efficiency, this article studied highway tollgate queuing discipline, which is one of the main contributing factors, aiming to reduce waiting time and reduce traffic jams. Based on the study of arrival rate of vehicles during peak hours and normal time, waiting time, queue length and the average efficiency of the service for each tollgate, a reasonable and feasible mathematical model is proposed. In this model, the vehicle arrival data obeys the Poisson distribution and transformed service time obeys the exponential distribution. Related data was given and the hypothesis test was conduced. By comparing rules before and after the establishment of the highway queuing model, the author offered suggestions on highway construction.

scholarly journals Staffing to Maximize Profit for Call Centers with Impatient and Repeat-Calling Customers

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Gong ◽  
Miao Yu ◽  
Jiafu Tang ◽  
Manru Li
Keyword(s):  
Waiting Time ◽  
Call Center ◽  
Numerical Experiments ◽  
Call Centers ◽  
Arrival Rate ◽  
Customer Behavior ◽  
Queuing Model ◽  
Direct Function ◽  
Many Server Queues

Motivated by call center practice, we study the optimal staffing of many-server queues with impatient and repeat-calling customers. A call center is modeled as an M/M/s+M queue, which is developed to a behavioral queuing model in which customers come and go based on their satisfaction with waiting time. We explicitly take into account customer repeat behavior, which implies that satisfied customers might return and have an impact on the arrival rate. Optimality is defined as the number of agents that maximize revenues net of staffing costs, and we account for the characteristic that revenues are a direct function of staffing. Finally, we use numerical experiments to make certain comparisons with traditional models that do not consider customer repeat behavior. Furthermore, we indicate how managers might allocate staffing optimally with various customer behavior mechanisms.

THE SIMULATION MODELLING METHODS FOR ORGANIZATION OF THE ONCOLOGY PATIENTS ROUTING

2021 ◽  
Author(s):  
Julya Zuenkova ◽  
◽  
Dmitry Kicha
Keyword(s):  
Waiting Time ◽  
Simulation Modeling ◽  
Simulation Modelling ◽  
X Ray ◽  
Modeling Methods ◽  
Care Services ◽  
Stay Time ◽  
Therapy Room ◽  
Oncology Care ◽  
Modelling Methods

Patient routing is a key tool for ensuring the availability and quality of cancer care, ensuring early detection of pathology and timely treatment. Mathematical and simulation modeling methods allow to predict the bottlenecks of patient flows and plan the optimal distribution of healthcare resources. Goal to optimize patients’ pathways for oncology care using the simulation modelling methods. Materials and methods Patient routing was presented in the logic of discrete events, the average resource utilization, the patient’s stay time were described, the bottlenecks of the system were determined. Simulation modeling methods were used to build the optimal organization of oncology care services in the region. Results The average waiting time at the pre-hospital stage was 10 days, the average hospitalization time for X-ray therapy was 24 bed days, the throughput of the X-ray therapy room was 6 patients per week, the average duration of the X-ray therapy session per patient was 10 minutes. With the help of simulation modeling methods, a multimodal system of oncodermatology care was created and put into practice, which allowed to reduce the patient’s waiting time for treatment to 0.7 days, increasing the throughput of the entire system.

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

scholarly journals Analysis of queue change of visitors and performace system in the Department of Population and Civil Regristation of Semarang City

2020 ◽  
Vol 202 ◽  
pp. 15005
Author(s):  
Sugito ◽  
Alan Prahutama ◽  
Dwi Ispriyanti ◽  
Mustafid
Keyword(s):  
Customer Satisfaction ◽  
Waiting Time ◽  
Public Service ◽  
Service Time ◽  
Service Sector ◽  
General Distribution ◽  
Queuing Systems ◽  
Queuing Model ◽  
The Public ◽  
Second Period

The Population and Civil Registry Office in Semarang city is one of the public service units. In the public service sector, visitor / customer satisfaction is very important. It can be identified by the length of the queue, the longer visitors queue this results in visitor dissatisfaction with the service. Queue analysis is one of the methods in statistics to determine the distribution of queuing systems that occur within a system. In this study, a queuing analysis as divided into two periods. The first period lasts from 2-13 March 2015, while the second period lasts November 16th to December 20th 2019. The variables used are the number of visitors and the service time at each counter in intervals of 30 minutes. The results obtained are changes in the distribution and queuing model that is at counter 5/6 and counter 10. The queuing model obtained at the second perideo for the number of visitors and the time of service with a General distribution. The average number of visitors who come in 30 minute intervals in the second period is more than the first period, this indicates an increase in visitors. The opportunity for service units is still small, the waiting time in the queue is getting smaller. This shows that the performance of the queuing system at the Semarang Population and Civil Registry Office is getting better.

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.

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