Hybrid Modelling and Simulation (M&S): Driving Innovation in the Theory and Practice of M&S

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordHybrid Simulation (HS) is the application of two or more simulation techniques (e.g., ABS, DES, SD) in a single M&S study. Distinct from HS, Hybrid Modelling (HM) is defined as the combined application of simulation approaches (including HS) with methods and techniques from the broader OR/MS literature and also across disciplines. In this paper, we expand on the unified conceptual representation and classification of hybrid M&S, which includes both HS (Model Types A-C), hybrid OR/MS models (D, D.1) and crossdisciplinary hybrid models (Type E), and assess their innovation potential. We argue that model types associated with HM (D, D.1, E), with its focus on OR/MS and cross-disciplinary research, are particularly well-placed in driving innovation in the theory and practice of M&S. Application of these innovative HM methodologies will lead to innovation in the application space as new approaches in stakeholder engagement, conceptual modelling, system representation, V&V, experimentation, etc. are identified

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this recordA symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.Erasmus

Hybrid simulation: Historical lessons, present challenges and futures

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Applying the Stress guidelines for reproducibility in modeling & Simulation: Application to a disease modeling case study

It is arguably difficult to reproduce the results of published work in Modeling &amp; Simulation (M&amp;S). Authors have certainly raised concerns about this issue and attempts by journals and conferences are being made to improve the situation. As part of a movement to tackle reproducibility in M&amp;S, the Strengthening The Reporting of Empirical Simulation Studies (STRESS) reporting checklists were introduced in 2018. The STRESS guidelines aimed to improve knowledge management in industry and to maximize the chance that all important M&amp;S details are included when writing up simulation research for publication. We extend this work by providing an applied example of using the STRESS-ABS checklist for documenting an Agent Based Simulation model. It is hoped that an applied example will both encourage and guide authors and practitioners to improve their reporting.</p