scholarly journals UTILITY 1 SERVER ON QUEUE SERVICE (STUDY: BANK ACCOUNT NUMBER CONVERSION)

2021 ◽  
Vol 2 (2) ◽  
pp. 187
Author(s):  
Johan Alfian Pradana
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Service Level ◽  
Bank Account ◽  
Queue System ◽  
Time Minimization ◽  
Queue Theory ◽  
And Performance ◽  
Number Of Customers

Fast-paced, precise demands and time minimization are dominant to support the service business. Service activities are always expected to be the best by customers. Especially ABC bank customers. Since the information about account conversion, many customers have come to the Bank. The server utility of the queue system plays an important role. One of them is about measuring the usefulness of the queue system, average expectations of waiting times, and expectations of the number of customers in the system. Services that focus on providing services always experience long lines. Therefore, the queue theory is used to assess utilities, waiting for time expectations, and expectations of customer numbers. Research methods using system performance. First, calculate the value of the speed, average service, service level, and performance of the queue system. The result is a queue system of 1 server on average - working with a utility value of 83.5% and the highest in the 4th week, with an average expectation - average waiting time of 0.428 or 25.6 minutes and an expectation of the number of customers in the system of 4.8 or 5 customers. The role of 1 server has not been practical to minimize waiting time expectations.

scholarly journals A queue theory in the cross-polarization of antenna in satellite communication

2021 ◽  
Vol 22 (2) ◽  
pp. 884
Author(s):  
Rio Mubarak ◽  
Setiyo Budiyanto ◽  
Putri Wulandari ◽  
Fajar Rahayu ◽  
Andi Adriansyah ◽  
...  
Keyword(s):  
Waiting Time ◽  
Queuing Theory ◽  
Satellite Communication ◽  
Service Level ◽  
Cross Polarization ◽  
Important Process ◽  
Queuing Model ◽  
Queue Theory ◽  
The Cross ◽  
And Performance

<span>Satellite communication is a telecommunications technique that uses satellites as a connecting component, for example VSAT. In antenna installation, there is an important process which is called the cross-polarization. Cross-polarization is one process that cannot be released inside installation of VSAT antennas for satellite communication. Sometimes, in this process, a user queue will occur. Queuing theory explain the process is done and also calculate the other factors that are in the process. By knowing queuing theory to the cross-polarization, it will be easy to know the efficiency of queuing theory in the cross-polarization. Based on the characteristics of the cross-polarization, user can be known the queuing model that used and performance of the queuing system. The queuing model for the cross-polarization, using Kendall notation, M/M/1. Based on the analysis that has been done; by using 1 server the value of service level (ρ) is 0.67, using 2 servers = 0.33 and 3 servers = 0.22. The waiting time in the queue is longer if using 1 server which is 0.67 hours or 40 minutes. If a satellite operator uses 2 servers, waiting time in the queue is 25 minutes and 3 servers is 2.8 minutes which means that there is almost no waiting time in the queue.</span>

scholarly journals OPTIMASI SISTEM ANTRIAN PADA LAYANAN KESEHATAN MASYARAKAT MENGGUNAKAN PENDEKATAN SIMULASI

2019 ◽  
Vol 3 (1) ◽  
pp. 14-22
Author(s):  
Widya Setia Findari ◽  
Yohanes Anton Nugroho
Keyword(s):  
Waiting Time ◽  
Six Sigma ◽  
Waiting Times ◽  
Community Health Center ◽  
Observation Data ◽  
Simulation Approach ◽  
Simulation Test ◽  
Queue System ◽  
Number Of Patients ◽  
Patient Service

Abstract : The purpose of this study is to optimize service time in a community health center. The average number of patients visiting is 100 to 300 per day. In certain units there is a heavy queue of patients which increases service waiting times, including registration units, inspection units, and pharmaceutical units. The initial observation data on the existing system shows the waiting time for patient services is 2,7 hours. This fact shows that the time of patient service on the existing system needs to be optimized so that the waiting time can be accelerated. This study offers a solution to optimize the service queue system using a simulation approach. The DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma method is used as a basis for analyzing the waiting time for services from an existing system. The results of the analysis are used in the simulation test to obtain improvement factors using several scenarios. The best simulation results are obtained with the scenario of adding operators in all units. Optimizing the waiting time of patient services using the best scenario simulation approach is shown by the decrease in waiting time of the queue system by 1,05 hours or 38,9% faster than the existing system.Keywords: System Optimizing; Public Health; Queue; Simulation; DMAIC Six SigmaAbstrak : Tujuan penelitian ini adalah untuk mengoptimalkan waktu tunggu pelayanan di sebuah pusat kesehatan masyarakat (Puskesmas). Rata-rata jumlah pasien yang berkunjung adalah 100 hingga 300 per hari. Pada beberapa unit tertentu terjadi antrian pasien yang padat sehingga meningkatkan waktu tunggu pelayanan, antara lain unit pendaftaran, unit pemeriksaan, dan unit farmasi. Data pengamatan awal pada sistem yang ada menunjukkan waktu tunggu pelayanan pasien adalah 2,7 jam. Fakta ini menunjukkan bahwa waktu pelayanan pasien pada sistem yang ada perlu dioptimalkan agar waktu tunggu dapat dipercepat. Penelitian ini menawarkan solusi optimalisasi sistem antrian pelayanan dengan menggunakan pendekatan simulasi. Metode DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma digunakan sebagai dasar analisis waktu tunggu pelayanan dari sistem yang sudah ada. Hasil analisis digunakan pada uji simulasi untuk mendapatkan faktor perbaikan dengan menggunakan beberapa skenario. Hasil simulasi terbaik diperoleh dengan skenario penambahan operator di semua unit. Optimasi waktu tunggu pelayanan pasien dengan menggunakan pendekatan simulasi skenario terbaik ditunjukkan oleh penurunan waktu tunggu sistem antrian sebesar 1,05 jam atau 38,9% lebih cepat dari sistem yang sudah ada.Kata kunci: Optimasi Sistem, Layanan Kesehatan, Antrian, Simulasi, DMAIC Six Sigma

Enhancing patient flow in emergency department (ED) using lean strategies–an integrated voice of customer and voice of process perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Alowad ◽  
Premaratne Samaranayake ◽  
Kazi Ahsan ◽  
Hisham Alidrisi ◽  
Azharul Karim
Keyword(s):  
Waiting Time ◽  
Emergency Services ◽  
Single Case ◽  
Patient Flow ◽  
Waiting Times ◽  
Small Sample ◽  
Content Type ◽  
Lean Tools ◽  
And Performance ◽  
Voice Of Customer

PurposeThe purpose of this paper is to systematically investigate the patient flow and waiting time problems in hospital emergency departments (EDs) from an integrated voice of customer (VOC) and voice of process (VOP) perspective and to propose a new lean framework for ED process.Design/methodology/approachA survey was conducted to better understand patients' perceptions of ED services, lean tools such as process mapping and A3 problem-solving sheets were used to identify hidden process wastes and root-cause analysis was performed to determine the reasons of long waiting time in ED.FindingsThe results indicate that long waiting times in ED are major concerns for patients and affect the quality of ED services. It was revealed that limited bed capacity, unavailability of necessary staff, layout of ED, lack of understanding among patients about the nature of emergency services are main causes of delay. Addressing these issues using lean tools, integrated with the VOC and VOP perspectives can lead to improved patient flow, higher patient satisfaction and improvement in ED capacity. A future value stream map is proposed to streamline the ED activities and minimize waiting times.Research limitations/implicationsThe research involves a relatively small sample from a single case study. The proposed approach will enable the ED administrators to avoid the ED overcrowding and streamline the entire ED process.Originality/valueThis research identified ED quality issues from the integration of VOC and VOP perspective and suggested appropriate lean tools to overcome these problems. This process improvement approach will enable the ED administrators to improve productivity and performance of hospitals.

Ferry Route Level of Service

1997 ◽  
Vol 1608 (1) ◽  
pp. 6-16 ◽  
Author(s):  
Joseph P. Savage
Keyword(s):  
Statistical Analysis ◽  
Waiting Time ◽  
Waiting Times ◽  
Absolute Number ◽  
Service Level ◽  
Washington State ◽  
Point Of View ◽  
Level Of Service ◽  
Transportation Service

Transportation service level measurements have been commonly used and accepted for highway systems, but similar service measures for ferry systems are less common, especially from the users’ point of view. An approach to measuring ferry route level of service is described that allows comparisons among ferry routes and between ferries and alternate modes such as highways (i.e., drive-around choices) and transit. The recommended approach focuses on excess user waiting times (excess delay) by mode (automobile, registered carpool or vanpool, bus, truck, and walk-on passenger), combined with calibrated relationships between volume-to-capacity (V/C) ratio and user delays for forecasting purposes. Data on waiting times for vehicles in the queues were collected on all ferry routes serviced by Washington State Ferries, and an extensive statistical analysis was performed to compute the relationships between V/C ratios and excess waiting times. Excess delay was defined as the waiting time for missed vessel sailings due to overloads, if any, after a ferry patron has arrived at the dock. User delays were expressed in two forms: absolute number of minutes of waiting time, and the number of boat sailings missed before boarding a ferry. The “boat wait” concept was introduced to differentiate between excess delays caused by congestion that prevents a driver from boarding the next ferry, and delays related to the amount of service provided on a route as reflected in the headways between vessels.

scholarly journals MENGUKUR EFEKTIVITAS DAN PEMODELAN SISTEM ANTRIAN PADA POLSEK PAMULANG KOTA TANGERANG SELATAN

2019 ◽  
Vol 1 (2) ◽  
pp. 1
Author(s):  
Yumniati Agustina ◽  
Aminudin .
Keyword(s):  
Steady State ◽  
Waiting Time ◽  
System Performance ◽  
System Modeling ◽  
Optimal Number ◽  
Theory Approach ◽  
Queue System ◽  
State Calculation ◽  
Research Findings ◽  
Queue Theory

Bureaucracy services in Indonesia haven’t given satisfaction to the society. One of services that   always complained is about queue problem. Queue system that isn’t optimal will give society’s dissatisfaction. Among bureaucracy systems that need to get concern is queue at PolsekPamulang-Kota Tangerang Selatan especially at SKCK service. So that, it is needed to be done a research to analyze and testing queue system modeling which has been applied by PolsekPamulang for SKCK service, analyze the effectiveness of the queue system performance of PolsekPamulang, and to determine the optimal number of services (counters)that should be used by PolsekPamulang. Method of research was done by queue theory approach. The research findings showed that queue of SKCK services at PolsekPamulang based on steady state calculation wasn’t effective.Because the steady state value still far from one number. The total of counter (a unit only) for SKCK service is still optimal, because of waiting time (queue) is still low. The length of queue on the system isn’t because of the large number of applicants, but it’s more focus on service standard to verify the document that needs more than 30 minutes for each document.

scholarly journals MODEL PENINGKATAN KNOWLEDGE SHARING BERBASIS MODAL SOSIAL DAN LEARNING ORGANIZATIONTERHADAP KINERJA AGENCY PADA PT PRUDENTIAL LIFE ASSURANCE

2017 ◽  
Vol 18 (2) ◽  
pp. 174
Author(s):  
Athoillah Athoillah
Keyword(s):  
Social Capital ◽  
Knowledge Sharing ◽  
Learning Organization ◽  
Test Results ◽  
Validity And Reliability ◽  
Life Assurance ◽  
And Performance ◽  
Number Of Customers ◽  
Positive Effect

The role of human resources reliable and professional in order to increase performance isneeded, because it will affect the survival of the company. The phenomenon that often occursin PT Prudential Life Assurance is the declining performance of the agency in the search for lifeinsurance customers indicated by the number of customers. The purpose of this research is toinvestigate and analyze the influence of social capital and learning organization of the agency’sperformance in PT Prudential Life Assurance with knowledge sharing as an intervening variable.The population is all the marketing power PT Prudential HD Agency Semarang are still active by67 employees. Given a population of only 67 employees, it deserves to be taken as a whole tobe sampled, so this study is a census study. A tool of analysis is path analysis, which previouslytested the validity and reliability and classic assumption test. The test results indicate socialcapital proved to have a positive effect and significant knowledge sharing. Learning organizationhas a positive and significant impact on knowledge sharing. Social capital, learning organizationand knowledge sharing proved to have a positive and significant impact on the performance ofthe agency. Knowledge sharing is not able to become an intervening variable between socialcapital with the performance of the agency. Knowledge sharing is able to become an interveningvariable between learning organization with the performance of the agency, meaning that thehigher awareness of the company in increasing the capacity of learning for employees, itwill raise the height of employees to share knowledge and it certainly will impact on the highperformance of the agency.Keywords: Social capital, learning organization, knowledge sharing and performance agency

scholarly journals SIMULASI ANTRIAN PELAYANAN BANK MENGGUNAKAN METODE EKSPONENSIAL

2018 ◽  
Author(s):  
Akim Manaor Hara Pardede
Keyword(s):  
Waiting Time ◽  
Single Channel ◽  
Waiting Times ◽  
Random Variable ◽  
Queueing Models ◽  
Modern Times ◽  
The World ◽  
Exponential Method ◽  
Retirement Funds ◽  
Number Of Customers

In modern times currently demanded everything must be quick and precise. This is because the more the increase of population in the world today that keeps growing. The queue is a aktifiitas where customers wait to obtain a service. In the system queue sometimes experiencing aproblem, the problem arises because the number of queued on the serve. A long queue occurs because of the large number of transactions at the teller either transfers, cash with drawals, mortgage payments, retirement funds are taking and receive cash retention. Queueing models discussed in this research is a queue where customers come in groups. The number of customers in each group is a random variable, and the time between the arrival of the exponential distribution is. On the research of Exponential method is used to calculate the time of service with a single channel to be able to observe how the performance of the system. Based on research results, gained sufficient servers to serve is as much as 3 servers, if only 1 server will result in average waiting times are too long resulting in the customer will get tired of waiting for a while if you're using 4 the server will result inan average waiting time of very little until the server is idle.

scholarly journals Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room

2021 ◽  
Vol 03 (01) ◽  
pp. 33-45
Author(s):  
Hassan Hijry ◽  
Richard Olawoyin
Keyword(s):  
Deep Learning ◽  
Emergency Room ◽  
Waiting Time ◽  
Waiting Times ◽  
Real Life ◽  
Healthcare Sector ◽  
Medical Attention ◽  
Queue System ◽  
Patient Waiting Time ◽  
Patient Waiting

Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.

OPTIMIZATION OF OVERFLOW POLICIES IN CALL CENTERS

2015 ◽  
Vol 29 (3) ◽  
pp. 461-471 ◽  
Author(s):  
G.M. Koole ◽  
B.F. Nielsen ◽  
T.B. Nielsen
Keyword(s):  
Performance Measures ◽  
Waiting Time ◽  
Optimal Policy ◽  
Call Center ◽  
Present Method ◽  
Call Centers ◽  
Waiting Times ◽  
Service Level ◽  
Markovian Approximation ◽  
Markov Decision

We examine how overflow policies in a multi-skill call center should be designed to accommodate performance measures that depend on waiting time percentiles such as service level. This is done using a discrete Markovian approximation of the waiting time of the first customers waiting in line. A Markov decision chain is used to determine the optimal policy. This policy outperforms considerably the ones used most often in practice, which use a fixed threshold. The present method can be used also for other call-center models and other situations where performance is based on actual waiting times and customers are treated in a FCFS order.

Trace nanoanalysis

1994 ◽  
Vol 52 ◽  
pp. 940-941
Author(s):  
D. E. Newbury ◽  
R. D. Leapman
Keyword(s):  
High Performance ◽  
Secondary Ion Mass Spectrometry ◽  
Detection Efficiency ◽  
Volume Element ◽  
And Performance ◽  
Trace Constituents ◽  
Secondary Ion ◽  
Concentration Levels ◽  
Structure Properties

Trace constituents, which can be very loosely defined as those present at concentration levels below 1 percent, often exert influence on structure, properties, and performance far greater than what might be estimated from their proportion alone. Defining the role of trace constituents in the microstructure, or indeed even determining their location, makes great demands on the available array of microanalytical tools. These demands become increasingly more challenging as the dimensions of the volume element to be probed become smaller. For example, a cubic volume element of silicon with an edge dimension of 1 micrometer contains approximately 5×1010 atoms. High performance secondary ion mass spectrometry (SIMS) can be used to measure trace constituents to levels of hundreds of parts per billion from such a volume element (e. g., detection of at least 100 atoms to give 10% reproducibility with an overall detection efficiency of 1%, considering ionization, transmission, and counting).

Sign in / Sign up

Export Citation Format

Share Document