Keynote Lectures

Modeling Engineering for Simulation of Complex Systems
Lin Zhang, 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

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.

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).

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