Converting High Level Models into DEVS Modeling and Simulation Applications

Wainer, Gabriel

doi:10.1007/978-3-030-17164-3_7

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Already in Zeigler [162] it is shown how abstract modeling views, such as the activity scanning approach, the process interaction approach, and the transaction-oriented method or equation-based models, can be mapped to the system formalisms. The same is true for other abstract M&S paradigms such as statecharts (Borland and Vangheluwe [16], Risco-Martín et al [124]) or signal floworiented methods (Cellier and Kofman [28]), and many others such as Petri-Nets, physical modeling approaches, or VHDL specifications as shown by Wainer [156]. Depending on the application domain, different modeling paradigms are used.…”

Section: Dynamic System Behavior System Formalisms and Modeling Appro...mentioning

confidence: 97%

An Architecture for Model Behavior Generation for Multiple Simulators

Folkerts

2024

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Due to the increasing variety of products, the complexity in the development of technical systems is growing more and more. Accordingly, the management of system variants is a central aspect in the investigation of multifaceted systems with methods of Modeling and Simulation (M&S). If the systems under investigation are modular-hierarchical, the System Entity Structure (SES)/Model Base (MB) framework is an adequate and established approach to variant management in M&S. This approach is based on a strict separation of system variants or system structures on the one hand and reusable and parameterizable basic components on the other hand. Different system structures, system variants, as well as different system parameterizations are called system configurations. While system configurations are represented in an SES, basic components of models are organized in an MB. The SES is a special tree structure and the basic components represent dynamic systems with defined input and output interfaces. The general SES/MB approach describes the SES as an ontology for cross-domain and simulator-independent specification of system configurations. The cross-domain nature of the SES is confirmed by a large number of practical applications. Previous software implementations of the SES/MB framework show that the organization of the MB in this respect is always simulator-specific. Furthermore, implementations of the SES/MB framework also exhibit simulator dependencies in SES, namely in referencing and parameterizing basic components of an MB. These limitations are not of concern if only one specific M&S environment is used. However, this work aims to overcome this limitation and support a model generation independent of an M&S environment.Complex and multifaceted systems, such as Cyber-Physical Systems (CPS), consist of components from different technical domains. Components are modeled and simulated with different and often domain-specific M&S environments. To generate an overall system model, components in the MB must be organized for different M&S environments. Furthermore, the specification of system configurations in the SES must also be truly simulator-independent.

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Section: Dynamic System Behavior System Formalisms and Modeling Appro...mentioning

confidence: 97%

An Architecture for Model Behavior Generation for Multiple Simulators

Folkerts

2024

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“…This work contrasted with the conventional the Intelligent Transportation System (ITS) which monitors and controls physical components, such as Connected Automated Vehicles (CAVs). A discrete-event and hybrid simulation framework based on Sim-Events was utilized to develop a traffic simulation model for an urban test facility [14]. This model was designed to aid in the evaluation of safety and performance of an Intelligent Transportation System through testing.…”

Section: Review Of Literaturementioning

confidence: 99%

“…This model was designed to aid in the evaluation of safety and performance of an Intelligent Transportation System through testing. The infrastructure encompasses several road and lane configurations, together with a comprehensive instrumentation system, specifically designed for the purpose of evaluating Connected and Autonomous Vehicles (CAVs) [14]. A research conducted by [15] examined traffic at a microscopic level.…”

Section: Review Of Literaturementioning

confidence: 99%

Discrete Event Simulation-Based Evaluation of a Single-Lane Synchronized Dual-Traffic Light Intersections

Calistus,

Martin,

Monday

et al. 2023

JCC

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This research involved an exploratory evaluation of the dynamics of vehicular traffic on a road network across two traffic light-controlled junctions. The study uses the case study of a one-kilometer road system modelled on Anylogic version 8.8.4. Anylogic is a multi-paradigm simulation tool that supports three main simulation methodologies: discrete event simulation, agent-based modeling, and system dynamics modeling. The system is used to evaluate the implication of stochastic time-based vehicle variables on the general efficiency of road use. Road use efficiency as reflected in this model is based on the percentage of entry vehicles to exit the model within a one-hour simulation period. The study deduced that for the model under review, an increase in entry point time delay has a domineering influence on the efficiency of road use far beyond any other consideration. This study therefore presents a novel approach that leverages Discrete Events Simulation to facilitate efficient road management with a focus on optimum road use efficiency.The study also determined that the inclusion of appropriate random parameters to reflect road use activities at critical event points in a simulation can help in the effective representation of authentic traffic models. The Anylogic simulation software leverages the Classic DEVS and Parallel DEVS formalisms to achieve these objectives.

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Converting High Level Models into DEVS Modeling and Simulation Applications

Cited by 2 publications

References 6 publications

An Architecture for Model Behavior Generation for Multiple Simulators

An Architecture for Model Behavior Generation for Multiple Simulators

Discrete Event Simulation-Based Evaluation of a Single-Lane Synchronized Dual-Traffic Light Intersections

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