Discrete-Event Applications with DEVS

Wainer, Gabriel

doi:10.1201/9781420053371-8

Cited by 6 publications

(5 citation statements)

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“…• Dynamics of Systems [16][17][18][19][20][21][22][23][24] • Modelling discrete events [25][26][27][28][29][30][31][32][33][34][35][36] • Modelling using agents [37][38][39][40][41][42][43][44] Figure 2. Simulation model as a decision support tool (c.f., [45]).…”

Section: Methods In Simulation Modelingmentioning

confidence: 99%

A Review of Modelling and SimulationTechniques for Life Support Systems and Queueing Systems

A Mageed

2024

Preprint

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This paper provides an overview of modelling and simulation methods for complex systems, including queuing and life support systems. To realize and capture every aspect of a system or situation, a model is used in place of the actual one. Using a model and simulation, we may create scenarios and make decisions. Computer experiments with the model can save costs, time, and effort once its validity has been shown. A model of the thing under study is created by computer simulation, which then runs the model on a digital computer and examines the results. It's interesting to note that a knowledge base of data includes information about predicted behaviour of the model as well as coordinated determination of external variables and parameters. Selecting the precise model creation technique is the first and most difficult task in simulation. A review of techniques used in simulation model execution (serial and parallel discrete-event simulation), simulation model analysis (calibration, validation, verification, goal-seeking), and conceptual, declarative, functional, constraint, and multimodal design are presented in this paper.

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Section: Methods In Simulation Modelingmentioning

confidence: 99%

A Review of Modelling and SimulationTechniques for Life Support Systems and Queueing Systems

A Mageed

2024

Preprint

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“…The events are the result of intentional actions or triggered upon verification of a certain condition. These actions promote system state changes at random time intervals 27 . Both the events and states that describe discrete event systems can be mapped into finite discrete sets in the modeling process 26 .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Both the events and states that describe discrete event systems can be mapped into finite discrete sets in the modeling process 26 . Using the model, input parameters (historical, real, or hypothetical) can be manipulated during experiments to observe the outputs 27 . These experiments can be performed using simulation, which aims to reproduce the modeled system dynamics behavior, allowing us to understand its functioning 28 .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Complexity measure based on sensitivity analysis applied to an intensive care unit system

Paiva,

Pacheco,

Barbosa

et al. 2023

Sci Rep

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This work proposes a system complexity metric and its application to Intensive Care Unit (ICU) system. The methodology for applying said complexity metric comprises: (i) parameters sensitivity indices calculation, (ii) mapping connections dynamics between system components, and (iii) system’s complexity calculation. After simulating the ICU computer model and using the proposed methodology, we obtained results regarding: number of admissions, number of patients in the queue, length of stay, beds in use, ICU performance, and system complexity values (in regular or overloaded operation). As the number of patients in the queue increased, the ICU system complexity also increased, indicating a need for policies to promote system robustness.

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“…A bag is a set with possible multiple occurrences of its elements e.g., {a,b,a,c} [30]. PDEVS also introduces the confluent transition function (

δ_{c o n}

), which decides the next state in case of collision between external and internal events (

e = t_{a} (s)

) without the need for a priority scheme.…”

Section: General Scenario and The Proposed Solutionmentioning

confidence: 99%

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

Artuñedo

Toro

Haber

2017

Sensors

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Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

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Discrete-Event Applications with DEVS

Cited by 6 publications

References 0 publications

A Review of Modelling and SimulationTechniques for Life Support Systems and Queueing Systems

A Review of Modelling and SimulationTechniques for Life Support Systems and Queueing Systems

Complexity measure based on sensitivity analysis applied to an intensive care unit system

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

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