Inside Discrete-Event Simulation Software: How it Works and Why it Matters

Author(s):  
T.J. Schriber ◽  
D.T. Brunner
Author(s):  
Martina Kuncova ◽  
Katerina Svitkova ◽  
Alena Vackova ◽  
Milena Vankova

The year 2020 was very challenging for everyone due to the COVID-19 pandemic. Many people turn their lives upside down from day to day. Politicians had to impose completely unprecedented measures, and doctors immediately had to adapt to the huge influx of patients and the massive demand for testing. Of course, not all processes could be planned completely efficiently, given that the situation literally changes from minute to minute, but sometimes better planning could improve the real processes. This contribution deals with the application of simulation software SIMUL8 to the analysis of the COVID-19 sample collection process in a drive-in point in a hospital. The main aim is to create a model based on the real data and then to find out the suitable number of other staff (medics) helping a doctor during the process to decrease the number of unattended patients and their waiting times.


2018 ◽  
Vol 64 (No. 4) ◽  
pp. 187-194 ◽  
Author(s):  
Armaghan Kosari Moghaddam ◽  
Hassan Sadrnia ◽  
Hassan Aghel ◽  
Mohammad Bannayan

A simulation model was developed for secondary tillage and sowing operations in autumn, using discrete event simulation technique in Arena<sup>®</sup> simulation software (Version 14). Eight machinery sets were evaluated on a 50-hectare farm. Total costs including fixed-costs, variable costs and timeliness costs were calculated for each machinery set. Timeliness costs were estimated for 21-years period on daily basis (Daily Work method) and compared with another method (Average Work method) based on the equation proposed by ASAE Standards, EP 496.3FEB2006. The Inputs of the model were machinery sets, field size, machines performances and daily soil workability state. The optimization criteria were the lowest costs and lowest standard deviation in daily work method plus the lowest costs based on average work method. The validity of the model was evaluated by comparing the output of the model with field observed data collected from various farms. Results revealed that there was no significant difference (P &gt; 0.01) between the observed and predicted finish day. 


2012 ◽  
Vol 433-440 ◽  
pp. 2480-2485
Author(s):  
Shi Fan Zhu ◽  
Jiang Jiang

The operating accuracy of the tractor driver directly affects the transportation efficiency and the airport security. The research of the human reliability is an important way to measure task performance and reduce errors. The main content of the study is to analyze the reliability of the driver of the towbarless tractor in Harbin International Airport. The task network is established using the method of HTN according to the towing actions. The reliabilities of each operation are calculated with the methods such as HCR and the characteristics of human behaviour (SOR). The simulation model is run for a hundred thousand times on the platform of the discrete event simulation software QUEST, and conclusions are brought out, one of them is the reliability of the driver is 99.666%. The main causes of the human error are the qualification and driving experience of the drivers and the circumstance of the airport.


Author(s):  
CHUNG-HORNG LUNG ◽  
JEFFERY K. COCHRAN ◽  
GERALD T. MACKULAK ◽  
JOSEPH E. URBAN

Software reuse has drawn much attention in computing research. Domain analysis is considered a prerequisite to effective reuse of existing software. Several approaches and methodologies have been proposed for domain analysis or domain modeling, but not many case studies have been reported in the literature. The first objective of this paper is to present the concept and practical experiences of a domain analysis approach in discrete-event simulation in manufacturing — generic /specific modeling. A second objective of this paper is to present a meta-model based on the generic/ specific approach from the software engineering perspective. The steps and knowledge required to build the model are described. Domain analysis lessons learned from the generic/specific approach in discrete-event simulation are discussed. Classification of this domain modeling approach was conducted through the Wartik and Prieto-Diaz criteria. The classification will facilitate the comparison with other domain analysis approaches. Similar modeling concepts or techniques may be beneficial to other researchers in their own application domains.


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