Airport Automated People Mover Systems: Analysis with a Hybrid Computer Simulation Model

2000 ◽  
Vol 1703 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Yi-Dar Lin ◽  
Antonio A. Trani
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Discrete Event ◽  
General Purpose ◽  
Simulation Software ◽  
Design Tool ◽  
Design Parameters ◽  
Computer Simulation Model ◽  
Hybrid Computer ◽  
Hybrid Computer Simulation

Automated people movers (APMs) have become an attractive solution to mobility problems associated with large airport terminals. A hybrid computer simulation model (called APMSIM) that has been developed to simplify the operational analysis of airport APM systems is presented. Given an airport passenger demand function, along with various APM vehicle technology and airport terminal characteristics, the model estimates timevarying level-of-service characteristics of the terminal including queues and processing times. The model simulates the movement of individual passengers and APM vehicles in the system network. APMSIM constitutes a design tool for airport planners and designers for determination of the sensitivity of system performance for a range of APM design parameters, examination of the flexibility of an APM system under given operational policy and network configurations, and estimation of APM vehicle energy consumption on the basis of network constraints and system characteristics. The model is a hybrid discrete-event and continuous simulation model developed in EXTEND, a general-purpose simulation software.

Using discrete-event simulation to increase the efficiency of point of distribution sites

2018 ◽  
Vol 16 (5) ◽  
pp. 279
Author(s):  
Patrick Glass, MS, MPH ◽  
Eric Dietz, PhD ◽  
Pamela Aaltenon, PhD, RN
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Discrete Event ◽  
T Test ◽  
Computer Simulation Model ◽  
Group A ◽  
The Mean ◽  
The Difference ◽  
Group B ◽  
Mean Time

Objective: The objective of this research was to develop a computer simulation model that will provide the most optimal allocation of resources for a point of distribution (POD) site.Design: A baseline assessment was conducted by participants establishing POD sections with no guidance from the investigator. A computer model was built with four stations: triage, registration, screening, and dispensing. The information from the computer simulation was used to design the allocation of volunteers for the experimental group. Once the data were collected, a two-sample t test was used to determine the significance of the difference between the average times of the two groups to complete the POD.Setting: The POD site was conducted indoors with volunteers acting as patients, and volunteer nursing students, and pharmacy students acting as POD workers. Volunteers were divided into two groups, group B, experimental and group A, control. Time was recorded using a digital time-stamp at the beginning and at the end of the POD.Interventions: The researcher inputted the total number of volunteers into the model, and the model generated the most applicable ratio for distribution of human capital: a one-to-one ratio of screeners to dispensers. Main outcome measures: The mean time for Group A was 4.55 minutes (95% CI: 4.27, 4.83). The mean time for group B was 3.05 minutes (95% CI: 2.79, 3.31). A two-sample t test and Analysis of Variance of these data show that the difference is meaningful (p < 0.001).Results: The results show that a discrete-event computer simulation can be used to identify the most efficient use of resources in order to decrease the amount of time that patients are required to participate.Conclusions: The discrete-event computer simulation model was found to be effective at identifying ways to increase efficiency and reduce the overall time required by patients to complete the POD.

Construct a computer simulation model to replicate CT operations for decreasing delayed procedure starts.

2014 ◽  
Vol 32 (30_suppl) ◽  
pp. 132-132
Author(s):  
Ranganath K. Iyer ◽  
Joseph Rodgers Steele ◽  
Habib Tannir ◽  
Steve Venable
Keyword(s):  
Simulation Model ◽  
Discrete Event ◽  
Wait Time ◽  
Simulation Models ◽  
Simulation Software ◽  
Operating Efficiency ◽  
Computer Simulation Model ◽  
Ct Scanner ◽  
Additional Time ◽  
The Impact

132 Background: Patients scheduled to undergo computed tomography (CT) should be treated expeditiously and not delayed owing to a lack of either CT scanner capacity or available staff. Delayed scanning affects both patients and staff in several ways. First, patients are unhappy that they have to wait. Also, delayed scanning makes patient late for their next appointments or other events, which affects the downstream departments’ capability to operate effectively and efficiently. In addition, radiologists and their staff have to commit additional time and resources to processing patients on time. Finally, variability in the placement of patients reduces the scanner’s operating efficiency. The aim of this initiative is to optimize the appointment template using simulation software to reduce the rate of delayed CT procedures by 25% or more by the end of 2014. Methods: To further understand the CT queuing process, we hired 2 graduate students to create a simulation model using the data collected from the operations study. The simulation study modeled patients’ experience from their arrival to discharge and the steps were: (a) performed elemental analysis for each process; (b) cceated value stream map; (c) created high-level simulation model and “mini model” using operational data. The simulation models were presented to department leaders, who approved them. The models clearly showed that the time patients spent on the CT scanner was the bottleneck. Results: Changes in the CT area that have impacted on-time starts and average wait time include: (a) new fast-track for no interview patients and (b) changes in staffing hours. Progress and improvement include (a.) On-time delays decreased by 18% and (b.) a verage wait decreased by 8 minutes (19%). Conclusions: Discrete event simulation accounts for the probabilities and uncertainties associated with the processes and helps create a visual model of the work area. This adds confidence to decision makers’ ability to make decisions that have high impact. Also, the models can be used to test changes in the processes and study the impact on other processes without making true operational changes that could potentially waste resources and time.

Validating the Vertical Dynamic Performance of a Multi-Wheeled Combat Vehicle Computer Simulation Model

2005 ◽  
Author(s):  
Matthew J. Hillegass ◽  
James G. Faller ◽  
Mark S. Bounds ◽  
Moustafa El-Gindy ◽  
Seokyong Chae
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Dynamic Performance ◽  
Performance Testing ◽  
Simulation Models ◽  
Simulation Software ◽  
Full Scale ◽  
Computer Simulation Model ◽  
Vehicle Model ◽  
Combat Vehicle

Performance testing is an important step in the development of any vehicle model. Generally, full-scale field tests are conducted to collect the dynamic response characteristics for evaluating the vehicle performance. However, with increases in computational power and the accuracy of simulation models, virtual testing can be extensively used as an alternative to the time consuming and costly full-scale tests, especially for severe maneuvers. Validation of the simulation results is critical for the acceptance of such simulation models. In this paper, a methodology for validating the vertical dynamic performance of a virtual vehicle has been discussed. The dynamic performance of a multi-wheeled combat vehicle model specially developed using a multi-body dynamics code was validated against the measured data obtained on the U.S. Army Aberdeen Test Center’s (ATC) test courses. The multi-wheeled combat vehicle variant computer simulation model was developed in TruckSim, a vehicle dynamic simulation software developed by the Mechanical Simulation Corporation. Prior to validating the model, the vehicle weights, dimensions, tires and suspension characteristics were measured and referenced in the specially developed computer simulation model. The data for the tire and suspension characteristics were acquired from the respective leading manufacturers in the form of look-up tables. The predictions of the vehicle vertical dynamics on different road profiles at various vehicle speeds were compared with the field test results. The time domain data for the vertical acceleration at the vehicle center of gravity, pitching, vehicle speed and the suspension/damper displacement were compared to analyze the feasibility of using the computer simulation models to predict the vertical dynamic performance of the vehicle. Based on the results it was found that the particular combat vehicle computer simulation model is capable of predicting the vertical dynamic performance characteristics.

Investigation into the Effects of Suspension Characteristics and Design Parameters on the Performance of Tracked Vehicles using an Advanced Computer Simulation Model

1988 ◽  
Vol 202 (3) ◽  
pp. 143-161 ◽  
Author(s):  
J Y Wong ◽  
J Preston-Thomas
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Design Parameters ◽  
Vehicle Design ◽  
Computer Simulation Model ◽  
Normal Range ◽  
Tracked Vehicles ◽  
Centre Of Gravity ◽  
Vehicle Weight ◽  
Advanced Computer

This paper describes the results of an investigation into the effects of the characteristics of the suspension system, initial track tension, vehicle weight and location of the centre of gravity on the tractive performance of tracked vehicles over unprepared terrain. The investigation was carried out using a newly developed computer simulation model NTVPM-86. The results show that the suspension characteristics, initial track tension and vehicle weight have noticeable effects on the mobility of tracked vehicles over marginal terrain, while the location of the centre of gravity, within the normal range, has a less significant influence on the tractive performance. It is demonstrated that the simulation model NTVPM-86 can play a significant role in the optimization of tracked vehicle design or in the evaluation of vehicle candidates for a given mission and environment.

scholarly journals Analysis and Optimization of Two Film-Coated Tablet Production Processes by Computer Simulation: A Case Study

Processes ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 67
Author(s):  
Stefanie Hering ◽  
Nico Schäuble ◽  
Thomas M. Buck ◽  
Brigitta Loretz ◽  
Thomas Rillmann ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Model Building ◽  
Discrete Event ◽  
Simulation Software ◽  
Labor Costs ◽  
Event Simulation ◽  
Processing Times ◽  
Production Processes ◽  
Coated Tablet ◽  
Pharmaceutical Production

Increasing regulatory demands are forcing the pharmaceutical industry to invest its available resources carefully. This is especially challenging for small- and middle-sized companies. Computer simulation software like FlexSim allows one to explore variations in production processes without the need to interrupt the running process. Here, we applied a discrete-event simulation to two approved film-coated tablet production processes. The simulations were performed with FlexSim (FlexSim Deutschland—Ingenieurbüro für Simulationsdienstleistung Ralf Gruber, Kirchlengern, Germany). Process visualization was done using Cmap Tools (Florida Institute for Human and Machine Cognition, Pensacola, FL, USA), and statistical analysis used MiniTab® (Minitab GmbH, Munich, Germany). The most critical elements identified during model building were the model logic, operating schedule, and processing times. These factors were graphically and statistically verified. To optimize the utilization of employees, three different shift systems were simulated, thereby revealing the advantages of two-shift and one-and-a-half-shift systems compared to a one-shift system. Without the need to interrupt any currently running production processes, we found that changing the shift system could save 50–53% of the campaign duration and 9–14% of the labor costs. In summary, we demonstrated that FlexSim, which is mainly used in logistics, can also be advantageously implemented for modeling and optimizing pharmaceutical production processes.

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