Keynote Lectures

Abstract
Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This capability allows analysts to compare alternative options to  improve a business process. A common roadblock for business process simulation is the fact that constructing high-fidelity simulation models is cumbersome and error-prone.

Modern information systems such as Enterprise Resource Planning or Customer Relationship Management systems store detailed execution logs of the business processes they support. These execution logs can be used to automatically discover simulation models. However, discovering high-accuracy simulation models from business process execution data turns out to be a challenging problem due to the numerous factors that affect the performance of real processes. One of the major challenges is accounting for various work patterns, including multitasking, task prioritization, batching, resource availability schedules, and time-varying resource performance (e.g. fatigue effects).

In this talk, I will give an overview of recent research in the field of automated discovery of business process simulation models. I will outline two approaches: one that uses process mining, curve fitting, and Bayesian optimization to discover and enhance a process model from an event log, and another approach that combines process mining with deep learning techniques. I will discuss the relative merits of these approaches and sketch open research challenges and questions.

Abstract
The industry is currently seeing a rapid development of cyber-physical system products containing integrated software, hardware, and communication components. The increasing system complexity in the automotive and aerospace industries are some examples. The systems that are developed have increasing demands of sustainability, dependability and usability. Moreover, lead time and cost efficiency continue to be essential for industry competitiveness. Extensive use of modeling and simulation - Model-Based Systems Engineering  tools - throughout the value chain and system life-cycle is one of the most important ways to effectively target these challenges. Simultaneously there is an increased interest in open source tools that allow more control of tool features and support, and increased cooperation and shared access to knowledge and innovations between organizations. Modelica is a modern, strongly typed, declarative, equation-based, and object-oriented (EOO) language for model-based systems engineering including modeling and simulation of complex cyber-physical systems  Major features are: ease of use, visual design of models with combination of lego-like predefined model building blocks, ability to define model libraries with reusable components, support for modeling and simulation of complex applications involving parts from several application domains, and many more useful facilities. The Modelica language is ideally suited for cyber-physical modeling tasks since it allows integrated modeling of discrete-time (embedded control software) and continuous-time (process dynamics, often for physical hardware). Modelica 3.3 extended the language with clocked synchronous constructs, which are especially well suited to model and integrate physical and digital hardware with model-based software.

This talk gives an overview and outlook of the OpenModelica environment – the most complete Modelica open-source tool for modeling, engineering, simulation, and development of systems applications (www.openmodelica.org), and its usage for sustainable cyber-physical system and digital twin development. Special features are MetaModeling for efficient model transformations, debugging support for equation-based models, support (via OMSimulator) for the Functional Mockup Interface for general tool integration and model export/import between tools, model-based optimization, as well as generation of parallel code for multi-core architectures.

Moreover, also mentioned is recent work to make an OpenModelica based tool chain for developing digital controller software for embedded systems, and in generating embedded controller code for very small target platforms like Arduino Boards with down to 2kbyte memory. This work has been extended in the recent EMPHYSIS project where the FMI standard has been extended into the eFMI standard for embedded systems.

Abstract
AnyLogic Cloud is a family of products that change the way an AnyLogic model lives. To play a model you can simply open the model page in the AnyLogic Cloud using a web browser. To share the model with colleagues or clients you can simply send a link to the model or embed the model in your web site. Model version management and model results storage ensure you do not waste time searching for the right experiment and model version, or repeating the same experiment unnecessarily. Built-in configurable dashboards, downloadable results, and RESTful API allow for access to the database with results providing easy ways to exploit the data for different purposes. Some of the most common usages include demonstrating results in a meeting, exporting results to business analytics or enterprise software, reinforcement and machine learning for advanced processes, AI training, integration with custom solvers, optimization engines etc.