Modeling Engineering for Simulation of Complex Systems
Lin Zhang, School of Automation Science and Electrical Engineering, Beihang University, China
Advances in Hybrid Simulation: Challenges and Research Opportunities from Philosophical, Conceptual and Technological Perspectives
Tillal Eldabi, University of Surrey, United Kingdom
Object Event Simulation
Gerd Wagner, Brandenburg University of Technology at Cottbus, Germany
Modeling Engineering for Simulation of Complex Systems
Lin Zhang
School of Automation Science and Electrical Engineering, Beihang University
China
Brief Bio
Lin Zhang is a professor of Beihang University, China. He received the B.S. degree in 1986 from Nankai University, China, the M.S. and the Ph.D. degree in 1989 and 1992 from Tsinghua University, China. His research interests include service-oriented modeling and simulation, cloud manufacturing, model engineering for simulation. He served as the President of the Society for Modeling and Simulation International (SCS), the executive vice president of China Simulation Federation (CSF). He is currently the president of Asian Simulation Federation (ASIASIM), a Fellow of SCS, ASIASIM and CSF, a chief scientist of the National High-Tech R&D Program and National Key R&D Program of China, and associate Editor-in-Chief and associate editors of 10 peer-reviewed journals. He authored and co-authored more than 200 papers, 10 books and chapters. He received the National Award for Excellent Science and Technology Books in 1999, the Outstanding Individual Award of China High-Tech R&D Program (863), 2001, the National Excellent Scientific and Technological Workers Awards in 2014.
Abstract
Simulation is an activity based on models. How to build a right model is the core issue in simulation. A model generally experiences requirement analysis, model design, model construction, VV&A, model implementation, and model maintenance. These processes compose a whole lifecycle of a model. Although importance of the engineering idea is gradually recognized in applications of the model lifecycle, currently still lacks complete theory and technology system and philosophy. Model Engineering (ME) aims at setting up a systematic, normalized and quantifiable engineering methodology to manage the data, knowledge, activities, processes and organizations/people involved in the whole life cycle of a model, in order to obtain a right model with the minimum cost. This lecture will discuss the challenges involved in the model lifecycle of a complex system, such as the complexity of evolution process of a model, the model reuse problem, the multidisciplinary collaboration in model development and management, etc. Key technologies of model engineering, e.g. model description languages, model management, service-oriented model composition, quantitative analysis and evaluation, and etc., will be introduced.
Advances in Hybrid Simulation: Challenges and Research Opportunities from Philosophical, Conceptual and Technological Perspectives
Tillal Eldabi
University of Surrey
United Kingdom
Brief Bio
Tillal Eldabi is a senior lecturer at Surrey Business School (University of Surrey). His research is mostly focusing on developing frameworks for Hybrid Simulation for modelling complex systems with special emphasis on aspects of modelling healthcare systems. In that regard, he developed tailormade modelling packages to support health economists and clinicians to decide on the best treatment programs. He published widely in highly ranked journals and conferences. He gained funding from national and international research councils such as EPSRC (UK), Qatar National Foundations, British Council, and UNDP – all related to modelling healthcare or Higher Education enhancement. His central believe is that technology and people should work together to enhance productivity and sustainability.
Abstract
Most authors tend to define Hybrid Simulation (HS) as a joint up simulation approach which links two or more simulation techniques (namely, Discrete Event Simulation, System Dynamics, and Agent Based Simulation). Whilst it is commonly argued that HS has been in existence for more than 5 decades, the last decade witnessed a significant surge in HS literature that is more problem-driven rather than mere technical experimentation. The benefits of Hybrid Simulation are well recorded in academic literature. HS is known to offer deeper insights into the real-life system as it allows modellers to assess its inherent problems from different dimensions. This talk provides background to HS, associated challenges, and their respective research opportunities at each of the steps in a typical HS lifecycle (particularly, Philosophical Dovetailing, Conceptual Modelling, Tools Linking, and the Human Factor).
Object Event Simulation
Gerd Wagner
Brandenburg University of Technology at Cottbus
Germany
Brief Bio
Gerd Wagner is Professor of Internet Technology at Brandenburg University of Technology, Cottbus, Germany. After studying Mathematics, Philosophy and Informatics in Heidelberg, San Francisco and Berlin, he (1) investigated the semantics of negation in knowledge representation formalisms, (2) developed concepts and techniques for agent-oriented modeling and simulation, (3) participated in the development of a foundational ontology for conceptual modeling, the Unified Foundational Ontology (UFO), and (4) created a new Discrete Event Simulation paradigm, Object Event Modeling and Simulation (OEM&S), and a new process modeling language, the Discrete Event Process Modeling Notation (DPMN). Much of his recent work on OEM&S and DPMN is available from sim4edu.com and dpmn.info
Abstract
Object Event Simulation (OES) is a new Discrete Event Simulation (DES) paradigm combining object-oriented modeling and event-based simulation (with event scheduling). An OES design model, providing a computationally complete description of a DES model, consists of an information design model and a process design model. The information design model specifies the types of objects and events that occur in the process design model, while the process design model defines causal regularities involving events as causes and object state changes as well as follow-up events as effects. We show how to use UML Class Diagrams and extended Event Graphs for making OES information and process models and use the JavaScript-based simulation framework OESjs for implementing them. Like Petri Nets and DEVS, OES has a formal semantics. But while Petrie Nets and DEVS are purely computational formalisms without an ontological foundation, OES is based on the ontological categories of objects, events and causal regularities.
The keynote presentation on "Object Event Simulation" is available here: https://dpmn.info/reading/SIMULTECH2020