System modeling in SYsML and system analysis in Arena

Batarseh, Ola; McGinnis, Leon F.

doi:10.1109/wsc.2012.6465139

Cited by 32 publications

(14 citation statements)

References 4 publications

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“…In [3] , system models defined in SysML are translated to be simulated using Arena simulation software. SysML models are not enriched with simulation-specific properties, while emphasis is given to system structure rather than system behavior.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, as SysML models in general cannot be directly simulated [3,34] and different types of system models are better simulated with specific simulation environments [37] , the simulation environment should be customized, depending on the system domain (e.g., EIS). In order to utilize model transformation tools, the simulation environment should support input in a model format (according to a meta-model), preferably defined in terms of Meta-Object Facility (MOF).…”

Section: Stakeholders Process and Artifactsmentioning

confidence: 99%

“…A common way for deriving such estimations is simulation. Thus, there is currently strong interest in generating simulation models from SysML models [3,4] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simulating simulation-agnostic SysML models for enterprise information systems via DEVS

Tsadimas

Kapos

Dalakas

et al. 2016

Simulation Modelling Practice and Theory

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Section: Related Workmentioning

confidence: 99%

Section: Stakeholders Process and Artifactsmentioning

confidence: 99%

See 1 more Smart Citation

Simulating simulation-agnostic SysML models for enterprise information systems via DEVS

Tsadimas

Kapos

Dalakas

et al. 2016

Simulation Modelling Practice and Theory

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“…In the context of the model-driven architecture, there exist a variety of modelling and transformation methods, either extending existing modelling languages (e.g. [16][17][18]) or providing their own meta-models (e.g. [19,20]), for the generation of discrete event simulations.…”

Section: Scientific Approachesmentioning

confidence: 99%

GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes

Lütjen¹,

Rippel²

2015

Int J Adv Manuf Technol

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The adequate planning of production processes is a big challenge, in which model-based planning concepts help to manage and to structure the accruing data. By using conceptual process models, like Business Process Modelling Notation, Event-driven Process Chains, etc., the consistent description of material flows is often neglected, meaning that the transformation into material flow simulation models is often impossible. Complex production processes with branched material flows are particularly difficult to model in a simulation driven way. This paper presents the modelling concept GRAMOSA (graphical modelling and simulationbased analysis) as an integrated approach for the material flow-oriented modelling of complex production processes. A new notation as part of an overall factory data model is developed. By using GRAMOSA, the factory data model can be transformed directly into executable simulation models. This concept of automated model generation saves time and reduces transformation failures in comparison to manual programming. The modelling concept and its simulation feasibility is evaluated by a use case, which represents a flexible flow shop scenario with 3×3 paralleled machines and inventory control strategy.

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“…Here, it is specifically the concept that a descriptive model of a system (e.g., in SysML) can be used to generate a model for analysis using a particular computational formalism (e.g., simulation or optimization). In practice, a domain-specific model using a SysML stereotype is used as input to the transformation, which then outputs the structure of the analysis model within its formalism, as well as populates the analysis model with data (Batarseh & McGinnis, 2012). Model transformation is an emerging field of research that is increasingly important in MBSE.…”

Section: Idef Versionsmentioning

confidence: 99%

Multi-Level Modeling of Complex Socio-Technical Systems - Phase 1

Rouse¹,

Bodner²

2013

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE JUN 20132. REPORT TYPE SUPPLEMENTARY NOTES ABSTRACTThis report presents a conceptual framework for multi-level modeling of complex socio-technical systems, provides linkages to the historical roots and technical underpinnings of this framework, and outlines a catalog of component models for populating multi-level models. This includes a description of the "systems movement," a summary of philosophical underpinnings, a review of seminal concepts, an overview of complex systems, discussion of complex adaptive systems, and contrasts of a range of systems approaches. Alternative modeling frameworks, including multi-level modeling frameworks, problem structuring methods, and computational representations, are also addressed. A proposed framework is presented for multi-level modeling of socio-technical systems, including discussion of the phenomena typically associated with each level, as well as a wide range of models of human behavior and performance. A comparison is provided of multi-level representations of the domains of healthcare delivery, energy consumption, and military operations. An illustrative example is presented focused on counterfeit parts in the military supply chain, in terms of both the consequences of such parts and interdicting the motivations to counterfeit. Finally, a wide range of fundamental research issues underlying multi-level modeling of complex systems is summarized. NO WARRANTY THIS STEVENS INSTITUTE OF TECHNOLOGY AND SYSTEMS ENGINEERING RESEARCH CENTER MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. STEVENS INSTITUTE OF TECHNOLOGY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. STEVENS INSTITUTE OF TECHNOLOGY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT.This material has been approved for public release and unlimited distribution except as restricted below.Internal use by SERC, SERC Collaborators and originators :* Permission to reproduce this material and to prepare derivative works from this material for internal use is granted, pro...

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System modeling in SYsML and system analysis in Arena

Cited by 32 publications

References 4 publications

Simulating simulation-agnostic SysML models for enterprise information systems via DEVS

Simulating simulation-agnostic SysML models for enterprise information systems via DEVS

GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes

Multi-Level Modeling of Complex Socio-Technical Systems - Phase 1

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