ECMS 2023 is happy to announce our two keynote speakers:
Professor at RWTH Aachen University, Germany, leading the Process and Data Science (PADS) group
Chief Scientist at Celonis
Title of talk
Towards More Realistic Simulation Models Using Object-Centric Process Mining
Abstract
Discrete-event simulation has been around for over half a century with applications in production, healthcare, logistics, transportation, etc. However, it is still challenging to create a reliable simulation model that mimics the actual process well and allows for “what-if” questions. Process mining allows for the automated discovery of stochastic process models using event data extracted from information systems. This technology is one of the key enablers for creating digital shadows and digital twins of operational processes. However, traditional process mining focuses on individual cases (e.g., an order, a patient, or a train) with events just referring to a single object (the case). Therefore, the discipline is moving to Object-Centric Process Mining (OCPM), where events can refer to any number of objects. Based on research on OCPM and the prototypes developed, now also commercial software vendors are embracing OCPM as is illustrated by Celonis Process Sphere, which allows for the discovery and analysis of object-centric process models. We believe that OCPM will help to create much more realistic simulation models. Whereas process discovery is backward-looking, with object-centric simulation models, we can also support forward-looking forms of process mining. In his keynote, prof. Wil van der Aalst, IFIP Fellow, IEEE Fellow, ACM Fellow, also known as “Godfather of Process Mining”, and one of the most cited computer scientists in the world (top-10 worldwide according to Research.com) will present how Object-Centric Process Mining will change the world of discrete-event simulation.
Short Bio:
Prof.dr.ir. Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer FIT, and a member of the Board of Governors of Tilburg University. His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 900 articles and books and is typically considered to be in the top-15 of most cited computer scientists with an H-index of 170 and more than 130.000 citations. Van der Aalst is an IFIP Fellow, IEEE Fellow, ACM Fellow, and received honorary degrees from the Moscow Higher School of Economics (Prof. h.c.), Tsinghua University, and Hasselt University (Dr. h.c.). He is also an elected member of the Royal Netherlands Academy of Arts and Sciences, the Royal Holland Society of Sciences and Humanities, the Academy of Europe, the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts, and the German Academy of Science and Engineering. In 2018, he was awarded an Alexander-von-Humboldt Professorship.
Professor of Operational Research at University of Southampton, United Kingdom
Editor-In-Chief of the Journal of Simulation
Title of talk
Making Decisions with Simulation
Abstract
Simulation models mimic real-world situations and enable experimentation with different system settings and designs. This can be done much more quickly and cheaply than experimenting in real-life, and experimentation and optimisation of simulation models has been in use for several decades. What has changed in recent years is the increased prominence of the idea of a digital twin. While the term digital twin has a variety of definitions dependent on your research area, within our community it gives the promise of using simulation for operational decision-making. The talk will use two examples, from manufacturing and healthcare, to describe advances in the use of simulation for real-time decision-making using digital twins. The first of these investigates the use of fast simulation optimisation to find the optimal repair strategy to use when several machines have broken down simultaneously on a production line. In the second example, which models a hospital emergency department, we discuss automatic parameterisation using standard hospital data, including process mining for determining the flow of patients through the department and statistical models to estimate arrival rates. These two examples will help to highlight open research questions in real-time decision making via simulation.