Panel on grand challenges for modeling and simulation

Taylor, Simon J. E.; Fujimoto, Richard M.; Page, Ernest H.; Fishwick, Paul A.; Uhrmacher, Adelinde M.; Wainer, Gabriel

doi:10.1109/wsc.2012.6465310

Cited by 14 publications

(9 citation statements)

References 39 publications

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“…DS has been widely applied in areas such as defense (Dahmann et al, 1997), combat modelling (Tolk, 2012) and in building large-scale network simulations (Taylor et al 2012a). However, despite the increasing number of standards, models and architectures which are available for determining how and when a DS can be used for SCS (Bandinelli et al, 2006) and the potential benefits of DSCS (Table 1), there is slow uptake in the industry (Boer et al, 2009;Robinson, 2005).…”

Section: Impleme Ntationmentioning

confidence: 99%

Distributed Approaches to Supply Chain Simulation

Mustafee

Katsaliaki

Taylor

2021

ACM Trans. Model. Comput. Simul.

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The field of Supply Chain Management (SCM ) is experiencing rapid strides in the use of Industry 4.0 technologies and the conceptualization of new supply chain configurations for online retail, sustainable and green supply chains, and the Circular Economy. Thus, there is an increasing impetus to use simulation techniques such as discrete-event simulation, agent-based simulation, and hybrid simulation in the context of SCM. In conventional supply chain simulation, the underlying constituents of the system like manufacturing, distribution, retail, and logistics processes are often modelled and executed as a single model. Unlike this conventional approach, a distributed supply chain simulation (DSCS) enables the coordinated execution of simulation models using specialist software. To understand the current state-of-the-art of DSCS, this paper presents a methodological review and categorization of literature in DSCS using a framework-based approach. Through a study of over 130 articles, we report on the motivation for using DSCS, the modelling techniques, the underlying distributed computing technologies and middleware, its advantages and a future agenda, and also limitations and trade-offs that may be associated with this approach. The increasing adoption of technologies like Internet-of-Things and Cloud Computing will ensure the availability of both data and models for distributed decision-making, which is likely to enable data-driven DSCS of the future. This review aims to inform organizational stakeholders, simulation researchers and practitioners, distributed systems developers and software vendors, as to the current state-of-the art of DSCS, and which will inform the development of future DSCS using new applied computing approaches.

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Section: Impleme Ntationmentioning

confidence: 99%

Distributed Approaches to Supply Chain Simulation

Mustafee

Katsaliaki

Taylor

2021

ACM Trans. Model. Comput. Simul.

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“…Simulation run-time efficiency challenges are also widely recognized [23,40,41]. Specialized formalisms, abstractions, or tools can reduce simulation execution time compared to general purpose simulation tools [42][43][44][45].…”

Section: Generating and Evaluating Large-scale Simulationsmentioning

confidence: 99%

A model-driven approach to interoperability between simulation and optimization for production and logistics systems

Sprock¹

2020

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Simulation and optimization must be used together to design and operate large-scale, complex systems. Integrating them presents a number of conceptual and technical problems, which can be addressed with more expressive, computer-interpretable system models. This paper presents a model-driven approach to integrating simulation and optimization methods by exchanging system designs that specialize analysis abstractions, both defined in the Systems Modeling Language (SysML). The analysis abstractions enable translation of system models to corresponding analysis models and tool interfaces. This approach (model-driven system-analysis integration) is demonstrated by developing a multi-fidelity, multi-method simulation optimization methodology and applying it to a supply chain design case study.

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“…To revisit Dagstuhl’s grand challenge themes in this second decade of the 21st century, several high-profile panels were organized between 2012 and 2013. These took place at the 2012 Winter Simulation Conference in Berlin, Germany, 7 the Symposium on Theory of Modeling and Simulation (TMS’13) during SpringSim 2013 in San Diego, USA, 8 the ACM SIGSIM Conference on Principles of Discrete Simulation in Montreal, Canada, 9 and the 2013 Winter Simulation Conference in Washington DC, USA. 10 This article presents an extended version of the panel held at the Symposium on Theory of Modeling and Simulation (TMS’13) and gives a diverse range of grand challenge themes for M&S that include Big Simulation, human behavior, composability, cloud-based M&S, reproducibility in M&S research and the democratization of M&S.…”

Section: Introductionmentioning

confidence: 99%

Grand challenges for modeling and simulation: simulation everywhere—from cyberinfrastructure to clouds to citizens

et al. 2015

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Modeling & Simulation (M&S) is making successful contributions to different areas in industry and academia. However, there are certain key issues that are preventing the field from addressing larger domains and from achieving wide-scale impact. Formulating these as grand challenges arguably focuses attention on these key issues and may bring a critical mass of effort to bear that could result in a major leap forward. This article is one of several concurrent activities aimed at reinvigorating the debate on grand challenges in M&S. These grand challenges include Big Simulation, human behavior, composability, cloud-based M&S, reproducibility in M&S research and the democratization of M&S. Two themes emerge: the need for large-scale cloud-based cyberinfrastructures for M&S and the democratized access to M&S and its outputs.

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Panel on grand challenges for modeling and simulation

Cited by 14 publications

References 39 publications

Distributed Approaches to Supply Chain Simulation

Distributed Approaches to Supply Chain Simulation

A model-driven approach to interoperability between simulation and optimization for production and logistics systems

Grand challenges for modeling and simulation: simulation everywhere—from cyberinfrastructure to clouds to citizens

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