Variantenmanagement in der Modellbildung und Simulation unter Verwendung des SES/MB Frameworks

Schmidt, A.

doi:10.11128/fbs.30

Cited by 12 publications

(70 citation statements)

References 26 publications

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“…The basic SES/MB framework introduced in [3] was extended by new modeling features, methods and components [4,5], such as an Experiment Control (EC) and an Execution Unit (EU) as shown in Figure 1. In this eSES/MB infrastructure, the EC uses an interface to the SES and its methods to derive goal-driven system configurations and to generate models, which are executed by the EU.…”

Section: Ses/mb Basics and The Eses/mb Infrastructurementioning

confidence: 99%

“…Figure 4 depicts the SES and its representation in SESToPy. The SES uses some extensions introduced in [5]. In addition to the different system configurations, essential parts for the configuration of simulation experiments are defined.…”

Section: Variant Modeling With Sestopymentioning

confidence: 99%

“…In contrast to 150% modeling, in this paper we describe a method to define, generate and simulate well-tailored and therefore lean models by making use of the System Entity Structure / Model Base (SES/MB) approach. The SES/MB approach [3] originates in the systems theory community and has undergone many extensions over the years [4,5]. It allows platform-independent variability modeling with subsequent platform-dependent model generation of specific variants.…”

Section: Introductionmentioning

confidence: 99%

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Model Generation for Multiple Simulators Using SES/MB and FMI

Folkerts¹,

Pawletta²,

Deatcu³

2021

SNE

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This paper deals with the extension of a Python-based infrastructure for studying the characteristics and behavior of families of systems. The infrastructure allows automatic execution of simulation experiments with varying system structures as well as with varying parameter sets in different simulators. Special focus is put on the support of different simulation environments by creating models implementing the Functional Mockup Interface (FMI). Possible system structures and parameterizations are defined using a System Entity Structure (SES). The SES as a high level approach for variability modeling, particularly in simulation engineering, describes a set of system configurations, i.e. different system structures and parameter settings of system components. In combination with a Model Base (MB), executable models can be generated from an SES. Based on the extended SES/MB approach, tool-supported variability modeling and automatic model generation and execution in different simulation environments using FMI is described. This is done by means of an engineering application.

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Section: Ses/mb Basics and The Eses/mb Infrastructurementioning

confidence: 99%

Section: Variant Modeling With Sestopymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Generation for Multiple Simulators Using SES/MB and FMI

Folkerts¹,

Pawletta²,

Deatcu³

2021

SNE

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“…This can also be accomplished by means of MDE [40], or, alternatively, by combining it with other methods. In this context, the System Entity Structure (SES) [243,213,320] has become a popular method that supports managing a model base of component models. SES is a structural knowledge representation scheme that contains decomposition, taxonomy and coupling of a system based on components.…”

Section: Future Workmentioning

confidence: 99%

Methods for Hybrid Modeling and Simulation-Based Optimization in Energy-Aware Production Planning

Heinzl¹

2020

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Production Planning (PPC) optimization in the operation of industrial production plants enables to achieve energy savings and improve energy efficiency. To provide decision support in production planning, simulation-based methods are particularly well-suited for complex production system to assess different planning scenarios and evaluate them with regard to their energy demand. To this end, a hybrid discrete/continuous modeling approach based on the Discrete-Event System Specification (DEVS) is investigated in this thesis. Hybrid modeling allows to accurately capture material flows as discrete entities on one hand and continuous energy flows on the other hand, in the production as well as for other domains, such as heating or cooling, up to the thermal building envelope, including their dynamic interactions. A meta-heuristic optimization procedure for operative production planning evaluates energy efficiency together with other production goals (delivery tardiness, storage costs, etc.) as part of a multi-objective optimization. The procedure follows a hybrid Variable Neighborhood Search (VNS) meta-heuristic and uses the hybrid simulation for the energetic evaluation of production programs. The VNS uses special energy-related operators, for example to optimize the setup times of heating ovens, in order to save energy. Furthermore, to support the modeling process of such DEVS-based simulations in practical applications, a domain-specific model abstraction is designed, which allows to describe hybrid simulation models in an intuitive way. The method is based on a Model-Driven Engineering (MDE) process and defines a component-based meta-model for production systems. Concrete implementations can be derived from the abstract specification by means of model transformations. Proof-of-concept implementations are realized to evaluate the individual contributions and case studies from industry demonstrate the practical applicability. The hybrid simulation approach was implemented in its entirety as a simulator prototype, together with reusable model components, which can be used by practitioners to design new application models with reduced efforts. Altogether, these methods contribute to the integration of energy efficiency aspects into modern PPC systems and to increase energy efficiency in production. i Kurzfassung Im operativen Betrieb von industriellen Anlagen lassen sich durch optimierte Produktionsplanung und-steuerung (PPS) gezielt Energieeinsparungen erreichen und die Energieeffizienz verbessern. Zur Entscheidungsunterstützung bieten sich gerade bei komplexen Produktionssystemen simulationsbasierte Methoden an, die es ermöglichen, unterschiedliche Planungsszenarien zu vergleichen und hinsichtlich ihres Energieeinsatzes zu bewerten. Dafür wird in der vorliegenden Arbeit ein hybrider diskret/kontinuierlicher Modellierungsansatz untersucht, der auf der Discrete-Event System Specification (DEVS) basiert. Die hybride Modellierung erlaubt es einerseits die Materialflüsse als diskrete Entitäten, andererseits auch die zei...

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“…Figure 4 depicts the SES and its representation in SESToPy. The SES uses some extensions introduced in [13]. In addition to the different system configurations, essential parts for the configuration of simulation experiments are defined.…”

Section: Variant Modeling With Sestopymentioning

confidence: 99%

Python-based eSES/MB Framework: Model Specification and Automatic Model Generation for Multiple Simulators

Folkerts¹,

Pawletta²,

Deatcu³

et al. 2019

SNE

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This paper proposes a Python-based infrastructure for studying the characteristics and behavior of families of systems. The infrastructure allows automatic execution of simulation experiments with varying system structures as well as with varying parameter sets in different simulators. Possible system structures and parameterizations are defined using a System Entity Structure (SES). The SES is a high level approach for variability modeling, particularly in simulation engineering. An SES describes a set of system configurations, i.e. different system structures and parameter settings of system components. In combination with a Model Base (MB), executable models can be generated from an SES. Based on an extended SES/MB approach, an enhanced software framework is introduced that supports variability modeling and automatic model generation for different simulation environments. By means of an engineering application it is shown, how a set of Python-based open source software tools can be used to model an SES and to automatically generate and execute signal-flow oriented models.

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Variantenmanagement in der Modellbildung und Simulation unter Verwendung des SES/MB Frameworks

Cited by 12 publications

References 26 publications

Model Generation for Multiple Simulators Using SES/MB and FMI

Model Generation for Multiple Simulators Using SES/MB and FMI

Methods for Hybrid Modeling and Simulation-Based Optimization in Energy-Aware Production Planning

Python-based eSES/MB Framework: Model Specification and Automatic Model Generation for Multiple Simulators

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