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Keynote Lectures

Mine Your Simulation Model: Automated Discovery of Business Process Simulation Models from Execution Data
Marlon Dumas, University of Tartu, Estonia

The OpenModelica Environment and Its Use for Development of Sustainable Cyber-physical Systems and Digital Twins
Peter Fritzson, Linköping University, Sweden

AnyLogic Cloud: An Integrated Environment for the Entire Model Lifecycle
Gregory Monakhov, AnyLogic, United States


Mine Your Simulation Model: Automated Discovery of Business Process Simulation Models from Execution Data

Marlon Dumas
University of Tartu

Brief Bio
Marlon Dumas is Professor of Information Systems at University of Tartu, Estonia and co-founder of Apromore - a company dedicated to developing and commercializing open-source process mining solutions. His research focuses on data-driven methods for business process management, including process mining and predictive process monitoring. He is currently recipient of an Advanced Grant from the European Research Council with the mission of developing algorithms for automated discovery and assessment of business process improvement opportunities. During his career, he has published over 200 research publications, 10 US/EU patents, and a textbook that is used in around 300 universities worldwide (Fundamentals of Business Process Management).

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.



The OpenModelica Environment and Its Use for Development of Sustainable Cyber-physical Systems and Digital Twins

Peter Fritzson
Linköping University

Brief Bio

Peter Fritzson is Professor and research director of the Programming Environment Laboratory, at Linköping University. He is also vice director of the Open Source Modelica Consortium, vice director of the MODPROD center for model-based product development, (previously director of both) organizations he took initiative to establish. During 1999-2007 he served as chairman of the Scandinavian Simulation Society, and secretary of the European simulation organization, EuroSim. During 2000-2020 he was vice Chairman of the Modelica Association.

Prof. Fritzson's current research interests are in software technology, especially programming languages, tools and environments; parallel and multi-core computing; compilers and compiler generators, high level specification and modeling languages with special emphasis on tools for object-oriented modeling and simulation where he is one of the main contributors and founders of the Modelica language. Professor Fritzson has authored or co-authored 319 technical publications, including 21 books/proceedings.

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 (, 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.



AnyLogic Cloud: An Integrated Environment for the Entire Model Lifecycle

Gregory Monakhov
United States

Brief Bio
Gregory Monakhov is a Senior Technical Support Engineer at The AnyLogic Company. He supports industry leaders around the globe and has extensive knowledge of the AnyLogic Company’s flagship product AnyLogic – the leading simulation software for business applications.

Gregory has been working in the domain of simulation for over 6 years and is uniquely experienced in applying simulation in different business scenarios. He consults directly with AnyLogic and anyLogistix users as they realize the benefits of simulation modeling and maintains leading-edge knowledge of its application. He has also conducted dozens of training sessions on simulation and regularly delivers new simulation insights.

With the advent of the new product AnyLogic Cloud, Gregory is actively involved in its development and helps deliver all-tier support.

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.