scholarly journals Using discrete-event computer simulation to test control systems

1989 ◽  
Author(s):  
T. I. Miles ◽  
H. Siddeley
Keyword(s):  
Computer Simulation ◽  
Control Systems ◽  
Discrete Event ◽  
Test Control

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.

scholarly journals Hospital beds and personal protective equipment planning for COVID-19 pandemic: A computer simulation approach

2020 ◽  
Author(s):  
Yiruo Lu ◽  
Yongpei Guan ◽  
Jennifer Fishe ◽  
Thanh Hogan ◽  
Xiang Zhong
Keyword(s):  
Computer Simulation ◽  
Personal Protective Equipment ◽  
Census Data ◽  
Discrete Event ◽  
Hospital Setting ◽  
Case Fatality ◽  
Protective Equipment ◽  
Simulation Approach ◽  
Number Of Patients ◽  
Hospital Resources

Abstract Health care systems are at the frontline to fight the COVID-19 pandemic. An emergent question for each hospital is how many general ward and intensive care unit beds are needed and how much personal protective equipment to be purchased. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a computer simulation approach to evaluating bed utilizations and the corresponding supply needs based on the operational considerations and constraints in individual hospitals. We built a data-driven SEIR model which is adaptive to control policies and can be utilized for regional forecast targeting a specific hospital’s catchment area. The forecast model was integrated into a discrete-event simulation which modeled the patient flow and the interaction with hospital resources. We tested the simulation model outputs against patient census data from UF Health Jacksonville, Jacksonville, FL. Simulation results were consistent with the observation that the hospital has ample bed resources to accommodate the regional COVID patients. After validation, the model was used to predict future bed utilizations given a spectrum of possible scenarios to advise bed planning and stockpiling decisions. Lastly, how to optimally allocate hospital resources to achieve the goal of reducing the case fatality rate while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.

A Road Map for the Validation, Verification and Testing of Discrete Event Simulation

2011 ◽  
pp. 3306-3313 ◽  
Author(s):  
Evon M. O. Abu-Taieh ◽  
Asim Abdel Rahman El Sheikh
Keyword(s):  
Computer Simulation ◽  
Service Delivery ◽  
Discrete Event ◽  
Real Life ◽  
Safety Net ◽  
Simulation Technique ◽  
Road Map ◽  
Life Problems ◽  
Computer Simulation Technique ◽  
Task Oriented

The aim of this chapter is to give an elaborate reasoning for the motivation for Validation, Verification, and Testing (VV&T) in Simulation. Thereby, defining Simulation in its broadest aspect as embodying a certain model to represent the behavior of a system, whether that may be an economic or an engineering one, with which conducting experiments is attainable. Such a technique enables the management, when studying models currently used, to take appropriate measures and make fitting decisions that would further complement today’s growth sustainability efforts, apart from cost decrease, as well as service delivery assurance. As such, the Computer Simulation technique contributed in cost decline; depicting the “cause and effect,” pinpointing task-oriented needs or service delivery assurance, exploring possible alternatives, identifying problems, as well as proposing streamlined, measurable, deliverable, solutions, providing the platform for change strategy introduction, introducing potential prudent investment opportunities, and finally, providing a safety net when conducting training courses. Yet, the simulation development process is hindered due to many reasons. Like a rose, Computer Simulation technique, does not exist without thorns, of which the length, as well as the communication during the development life cycle. Simulation reflects real-life problems; hence, it addresses numerous scenarios with handful of variables. Not only is it costly, as well as liable for human judgment, but also, the results are complicated and can be misinterpreted.

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