scholarly journals PEMODELAN SISTEM ANTREAN PELAYANAN BUS JALUR BARAT TERMINAL TIRTONADI KOTA SURAKARTA DENGAN METODE BAYESIAN

2021 ◽  
Vol 10 (3) ◽  
pp. 466-475
Author(s):  
Nurul Khasanah ◽  
Sugito Sugito ◽  
Yuciana Wilandari
Keyword(s):  
System Performance ◽  
Prior Information ◽  
Bayesian Method ◽  
Service System ◽  
Performance Measure ◽  
The West ◽  
Queue System ◽  
Service Patterns ◽  
Bus Service ◽  
System Performance Measure

Tirtonadi is the largest bus station in Surakarta City. The departure line is devided into two lines, namely west line and east line. The west line serves buses to the west of Surakarta City. The number of buses that enter and leave the station every day causes bus queues. Modeling the queue system and analyzing the system performance measure aims to determine wether the bus service system is good or not. The queue system model is obtained by finding the distribution of arrival patterns and service patterns using the Bayesian method. This method is used because it combines the information from the current research and the prior information from the previous research. The queueing condition of the five lanes in the west line meets steady state conditions because the utility value is less than 1. The queue displant is First Come First Service (FCFS) with unlimited customers and unlimited calling sources. Based on the posterior distribution, the queue system of service bus is (GAMM/IG/1):(GD/∞/∞) for Solo-Jakarta-Bandung lane and Pedesaan lane, while for Solo-Purwokerto-Cilacap, Solo-Yogyakarta, and Solo-Semarang has the queue system (GAMM/GAMM/1):(GD/∞/∞). The queue system of service bus for each lane has good services based on the value of system performance measure. 

scholarly journals ANALISIS SISTEM PELAYANAN KERETA API DI STASIUN SEMARANG TAWANG MENGGUNAKAN PROSES BAYESIAN

2020 ◽  
Vol 9 (4) ◽  
pp. 495-504
Author(s):  
Lifana Nugraeni ◽  
Sugito Sugito ◽  
Dwi Ispriyanti
Keyword(s):  
Poisson Distribution ◽  
Posterior Distribution ◽  
Public Transportation ◽  
Bayesian Method ◽  
Service System ◽  
Likelihood Function ◽  
Performance Standard ◽  
Sample Distribution ◽  
Service Patterns ◽  
Transportation Facilities

Along with the times, transportation has progressed. Regarding the means of transportation, one of the phenomenon that is easily encountered in everyday life is the queue at public transportation facilities. One of the queues that occurred at public transportation facilities is  the train queue at Semarang Tawang Station. The number of trains that passes the station can cause the train service at the station busy. This study aims to see whether the train service system of Semarang Tawang Station is good or not. This can be consider by the queues method, determining the distribution of arrival patterns and service patterns to obtain a queues system model and a system performance standard. In this study, the distribution of arrival patterns and service patterns are determined by calculating the posterior distribution using the Bayesian method. The bayesian method was chosen because it is able to combine the sample distribution in the current study with the previous information for the same cases. The prior distribution and the likelihood function are the elements needed to obtain the posterior distribution. The distribution of arrival patterns and service patterns obtained from previous information follows the Poisson distribution. Based on the calculation of the posterior distribution, the result shows that the distribution of the arrival pattern is a discrete uniform distribution and the distribution of the service pattern is a Poisson distribution. The result shows that the train service system at Semarang Tawang Station has a model (Uniform Discrete / Gamma / 7: GD / ~ / ~) and has good service based on the performance values obtained.

Modeling and Analysis of Multi-Product Manufacturing Systems With Two Machines and One Buffer

2019 ◽  
Author(s):  
Jing Huang ◽  
Qing Chang ◽  
Jorge Arinez
Keyword(s):  
System Performance ◽  
Manufacturing Systems ◽  
Performance Measure ◽  
Buffer System ◽  
Product System ◽  
Modeling And Analysis ◽  
Product Manufacturing ◽  
Production Orders ◽  
System Performance Measure ◽  
Flow Conservation

Abstract The ability to process multiple product types is an important criterion for evaluating the flexibility of a manufacturing system. The system dynamics of a multi-product system is quite distinct from that of a single-product system. A modeling method for the multi-product system is proposed based on dynamic systems and flow conservation. Based on the model, this paper places its emphasis on the analysis of a two-machine-one-buffer system with two product variants. The system performance measure of a multi-product system is proposed based on production orders. The system performance of two-machine-one-buffer systems is discussed in full details. The conditions for the system achieving the best performance are derived. Finally, several numerical experiments are conducted to validate the propositions on two-machine-one-buffer system.

Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data

2021 ◽  
Author(s):  
Oliver van Zwanenberg ◽  
Sophie Triantaphillidou ◽  
Robin B. Jenkin ◽  
Alexandra Psarrou
Keyword(s):  
Spatial Frequency ◽  
Frequency Response ◽  
System Performance ◽  
Performance Measure ◽  
Natural Scenes ◽  
Natural Scene ◽  
Performance Measurements ◽  
Camera System ◽  
Edge Based ◽  
System Performance Measure

The Natural Scene derived Spatial Frequency Response (NS-SFR) is a novel camera system performance measure that derives SFRs directly from images of natural scenes and processes them using ISO12233 edge-based SFR (e-SFR) algorithm. NS-SFR is a function of both camera system performance and scene content. It is measured directly from captured scenes, thus eliminating the use of test charts and strict laboratory conditions. The effective system e-SFR can be subsequently estimated from NS-SFRs using statistical analysis and a diverse dataset of scenes. This paper first presents the NS-SFR measuring framework, which locates, isolates, and verifies suitable step-edges from captures of natural scenes. It then details a process for identifying the most likely NS-SFRs for deriving the camera system e-SFR. The resulting estimates are comparable to standard e-SFRs derived from test chart inputs, making the proposed method a viable alternative to the ISO technique, with potential for real-time camera system performance measurements.

That confounded system performance measure: A demonstration.

1959 ◽  
Vol 66 (3) ◽  
pp. 178-182 ◽  
Author(s):  
F. V. Taylor ◽  
H. P. Birmingham
Keyword(s):  
System Performance ◽  
Performance Measure ◽  
System Performance Measure

scholarly journals ANALISIS METODE BAYESIAN MENGGUNAKAN NON-INFORMATIF PRIOR UNIFORM DISKRIT PADA SISTEM ANTREAN PELAYANAN GERBANG TOL MUKTIHARJO

2021 ◽  
Vol 10 (3) ◽  
pp. 337-345
Author(s):  
Dini Febriani ◽  
Sugito Sugito ◽  
Alan Prahutama
Keyword(s):  
System Performance ◽  
Prior Distribution ◽  
Bayesian Method ◽  
Negative Binomial ◽  
Service System ◽  
Likelihood Function ◽  
System Model ◽  
Large Cities ◽  
The Road ◽  
On The Road

The growth rate of the traffic that is high resulting in congestion on the road network system. One of the government's efforts in addressing the issue with the build highways to reduce congestion, especially in large cities. One of the queuing phenomena that often occurs in the city of Semarang is the queue at the Toll Gate Muktiharjo, that the queue of vehicles coming to make toll payment. This study aims to determine how the service system at the Toll Gate Muktiharjo. This can be known by getting a queue system model and a measure of system performance from the distribution of arrival and service. The distribution of arrival and service are determined by finding the posterior distribution using the Bayesian method. The bayesian method combine the likelihood function of the sample and the prior distribution. The likelihood function is a negative binomial. The prior distribution used a uniform discrete. Based on the calculations and analysis, it can be concluded that the queueing system model at the Toll Gate Muktiharjo is a (Beta/Beta/5):(GD/∞/∞). The queue simulation obtained that the service system Toll Gate Muktiharjo is optimal based on the size of the system performance because busy probability is higher than jobless probability.  

scholarly journals ANALISIS ANTREAN BUS NONPATAS AKAP DAN AKDP JALUR TIMUR TERMINAL TIRTONADI KOTA SURAKARTA

2018 ◽  
Vol 7 (3) ◽  
pp. 303-313
Author(s):  
Rosalina Aprilda Sitomurang ◽  
Sugito Sugito ◽  
Moch. Abdul Mukid
Keyword(s):  
Steady State ◽  
System Performance ◽  
Human Life ◽  
Performance Measure ◽  
Queuing Systems ◽  
Service Capacity ◽  
Utility Value ◽  
Queue Model ◽  
System Performance Measure ◽  
First In First Out

The queuing system is a set of customers, services and a set of rules governing the arrival of its customers and services. Queue is a waiting phenomenon that is part of everyday human life. The queue is formed if the number of subscribers to be served exceeds the available service capacity. Queue phenomenon one of them seen in the queue nonpatas buses at Terminal Tirtonadi Surakarta. Nonpatas bus lanes studied include non-purpose buses Surabaya, Karanganyar, Wonogiri, Purwodadi and Pedesaan. The queue displant used is FIFO (First In First Out). For the five nonpatas bus lanes it meets steady state conditions because it has utility value less than 1. The selected model is a model that has the following 4 types of distributions: Erlang, Weibull, Gamma and Lognormal. The queue model generated for the five tracks (ERLA/ERLA/1):(GD/∞/∞) for Surabaya nonpatas buses, (ERLA/WEIB/1):(GD/∞/∞) for Karanganyar nonpatas buses, (GAMM/WEIB/1):(GD/∞/∞) for Wonogiri nonpatas buses, (ERLA/WEIB/1):(GD/∞/∞) for Purwodadi nonpatas buses, (WEIB/LOGN/1):(GD/∞/∞) for Pedesaan nonpatas buses. Based on the value of the system performance measure indicated that the five lines are queue system is good. Keywords: Beta, Erlang, FIFO, Gamma, Steady State Conditions, Lognormal, Queue Model, Queuing Systems, System Performance Measure, Weibull

scholarly journals Games People Play: Lessons on Performance Measure Gaming from New Zealand Comment on "Gaming New Zealand’s Emergency Department Target: How and Why Did It Vary Over Time and Between Organisations?"

2020 ◽  
Author(s):  
Lisa M. Lines
Keyword(s):  
Emergency Department ◽  
New Zealand ◽  
Performance Measurement ◽  
System Performance ◽  
Wait Time ◽  
Performance Measure ◽  
Health System Performance ◽  
Ed Visits ◽  
System Performance Measure ◽  
Over Time

For decades, observers have noted that gaming of performance measurement appears to be both endemic and endlessly creative. A recent study by Tenbensel and colleagues provides a detailed look at gaming of a health system performance measure—emergency department (ED) wait times—within four hospitals in New Zealand. Combined, these four hospitals handled more than 25% of the ED visits in the country each year. Tenbensel and colleagues examine whether the New Zealand ED wait time target was set appropriately and whether we can trust any performance measure statistics that are not independently verified or audited. Their thoughtprovoking examination is relevant to anyone working in quality improvement and provides a valuable set of tools for detecting gaming in performance measurement.

scholarly journals Characteristics of Changes of Transportation Performance for Pairs of Critical Edges

2018 ◽  
Vol 20 (3) ◽  
pp. 84-87
Author(s):  
Peter Czimmermann ◽  
Michal Kohani
Keyword(s):  
System Design ◽  
Public Service ◽  
System Performance ◽  
Service System ◽  
Transportation Network ◽  
Service System Design ◽  
The Creation ◽  
Random Failures

When the robustness of a public service system design is tested, we can often use scenarios where possible random failures can occur and they can influence the time the service is accessible which is provided for system users. The construction of a suitable scenario is based on the choice of links of the transportation network which influence the system performance in a substantial way. In such scenarios one or multiple arcs can be affected by this failure. In our contribution we present characteristics of pairs of critical arcs that can be used to develop an algorithm for the creation of critical scenarios.

Sign in / Sign up

Export Citation Format

Share Document