Modeling and Simulation of Versatile Technical Systems Using an Extended System Entity Structure/Model Base Infrastructure

Pawletta, Thorsten; Durak, Umut; Schmidt, Artur

doi:10.1016/b978-0-12-813543-3.00018-4

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…Section 2.2 discussed different levels of model abstraction when introducing MDE and MDA. Pawletta et al [114] as well as Jafer et al [70] state that variant or variability modeling can take place at different abstraction levels. For this purpose, they refer to the Meta-Object Facility (MOF) hierarchy [100], which has strong relations to the model classes defined by the MDA.…”

Section: Approaches From Software Engineeringmentioning

confidence: 99%

“…The SES/MB framework according to Zeigler et al [167] describes the integration of an SES with an MB. The SES/MB framework defines another transformation method, which is called build here following Pawletta et al [114]. With the build method, a complete simulation model is generated using the PES and basic models from the MB.…”

Section: Basics Of the Ses/mb Approachmentioning

confidence: 99%

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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: Approaches From Software Engineeringmentioning

confidence: 99%

Section: Basics Of the Ses/mb Approachmentioning

confidence: 99%

An Architecture for Model Behavior Generation for Multiple Simulators

Folkerts

2024

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“…The attached variables of a SES can specify static parameters or variable parameters. Possibilities of configuration-dependent parameterization using SES variables and SES functions are discussed in [19,36].…”

Section: Contributionmentioning

confidence: 99%

“…While widely used tools in this area (such as MATLAB/SIMULINK c or Modelica c [42]) already support well-defined approaches for modular and hybrid modeling of system dynamics, the authors estimate that an innovative methodology for variant management of models and simulation experiments as well as the automated generation of simulation models and simulation experiments is missing. How the SES-based modeling approach can be used in this context, either as a supplement or as an alternative in cooperation with the established tools in this field of application, has been published several times, for example in [43,36]. The further development of the method shown here is seen as a logical step toward better mastering the multiresolution modeling of complex and adaptive systems according to [44].…”

mentioning

confidence: 96%

Discrete-Event Simulation Model Generation based on Activity Metrics

Capocchi

Santucci

Pawletta

et al. 2020

Simulation Modelling Practice and Theory

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“…The EF implements the interface for a Simulation-Based Experiment (SBE). Inspired by Breitenecker's [1] approach to structuring SBEs, Pawletta et al [5] and Schmidt [7] introduced the concept of Simulation Method (SimMeth) and Experiment Method (ExpMeth). The SimMeth controls the execution of the simulation runs via a simulator and ExpMeths are arbitrary numerical methods.…”

Section: Introductionmentioning

confidence: 99%

Integrating Reinforcement Learning and Discrete Event Simulation Using the Concept of Experimental Frame: A Case Study With MATLAB/SimEvents

Pawletta,

Bartelt

2023

SNE

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Reinforcement Learning (RL) is an optimization method characterized by two interacting entities, the agent and the environment. The environment is a Markov Decision Process (MDP). The goal of RL is to learn how an agent should act to achieve a maximum cumulative reward in the long-term. In discrete-event simulation (DES), the dynamic behavior of a system is represented in a model (DESM) that is executed via a simulator. The concept of Experimental Frame (EF) provides a structural approach to separating the DESM into the Model Under Study (MUS) and its experimental context. Here, we explore the integration of a discrete event MUS as an environment for RL using the concept of EF. After discussing the methodological framework, a case study using MATLAB/Simulink and the SimEvents blockset is considered. The case study starts with an introduction of the discrete-event MUS for which a control strategy shall be developed. The MUS is reused in three experiments using specific EFs. First, an EF for the design of a heuristic control strategy with ordinary simulation runs is presented.Then, based on the methodological approach, specifics of the EF are considered when using a self-implemented Qagent and the RL toolbox of MATLAB/Simulink.

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Modeling and Simulation of Versatile Technical Systems Using an Extended System Entity Structure/Model Base Infrastructure

Cited by 4 publications

References 14 publications

An Architecture for Model Behavior Generation for Multiple Simulators

An Architecture for Model Behavior Generation for Multiple Simulators

Discrete-Event Simulation Model Generation based on Activity Metrics

Integrating Reinforcement Learning and Discrete Event Simulation Using the Concept of Experimental Frame: A Case Study With MATLAB/SimEvents

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