Zilverling

dr.ir. R.H. Bemthuis (Rob)

Assistant Professor
EEMCS-CS-PS
Zilverling 5010
r.h.bemthuis@utwente.nl
+31534897009
Business card

Rob Bemthuis is an Assistant Professor in the Pervasive Systems Research Group at the University of Twente (UT). He holds both a Master’s and Bachelor’s degree in Industrial Engineering and Management from the UT, with a specialization in production and logistics management. He earned his Ph.D. from the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at the same university. 

Rob’s research involves IoT- and Big Data-driven solutions to detect and manage emerging behaviors. He was a visiting scholar at the University of Southern Denmark (SDU) and the Karlsruhe Institute of Technology (KIT). Currently, he is principal investigator on the ECOLOGIC project, which is about sustainable construction logistics, and the related ECOCHECK project. His interests include multi-agent systems, logistics simulation, and applied process mining, with a strong focus on IoT-based approaches. 

Alongside his research, Rob actively volunteers at the University of Twente and in Enschede. He served as Treasurer of the PhD Network of the University of Twente (P-NUT) from 2019 to 2020, was a general board member from 2020 to 2021, and then Treasurer and Vice-President from 2021 to 2022. He also (co-)chaired the organizing committees of the PhD Day 2019, PhD & PDEng Day 2020, and PhD & PDEng Day 2021. 

Expertise

  • Computer Science

    • Process Mining
    • Models
    • Process Mining Technique
    • Case Study
    • Simulation Mode
    • Data Mining
    • Process Model
    • Industry Standard

Organisations

Publications

2025

Towards integrating process mining with agent-based modeling and simulation: State of the art and outlook (2025)Expert systems with applications, 281. Article 127571. Bemthuis, R. H. & Lazarova-Molnar, S.https://doi.org/10.1016/j.eswa.2025.127571A CRISP-DM-based methodology for assessing agent-based simulation models using process mining (2025)Journal of simulation, 1-22 (E-pub ahead of print/First online). Bemthuis, R. H., Govers, R. R. & Asadi, A.https://doi.org/10.1080/17477778.2025.2508245Towards Edge-Based Idle State Detection in Construction Machinery Using Surveillance Cameras (2025)[Working paper › Preprint]. ArXiv.org. Küpers, X., Klein Brinke, J., Bemthuis, R. H. & Durmaz - Incel, Ö.https://doi.org/10.48550/arXiv.2506.00904Lost in Models? Structuring Managerial Decision Support in Process Mining with Multi-criteria Decision Making (2025)[Working paper › Preprint]. ArXiv.org. Bemthuis, R. H.https://doi.org/10.48550/arXiv.2505.10236IoT-Enabled Multi-Agent Simulation for Hazard Detection and Safety in Construction (2025)Procedia computer science, 257, 354-363. Sepanosian, T. & Bemthuis, R. H.https://doi.org/10.1016/j.procs.2025.03.047Consumer-Driven Sustainability Transitions in the Food Supply Chain: An Initial Simulation Framework (2025)Procedia computer science, 257, 344-353. Chaudhuri, A., Chilekampally, R., Manu Krishna Bindhu, N., Andrea, M. & Bemthuis, R. H.https://doi.org/10.1016/j.procs.2025.03.046Lost in Models? Structuring Managerial Decision Support in Process Mining with Multi-criteria Decision Making (2025)In Intelligent Information Systems: CAiSE 2025 Forum and Doctoral Consortium, Vienna, Austria, June 16-20, 2025, Proceedings (pp. 29-36) (Intelligent Information Systems; Vol. 557). Springer. Bemthuis, R. H.https://doi.org/10.1007/978-3-031-94590-8_4Process Mining for Demographic Insights: A Subpopulation Analysis in Healthcare Pathways (2025)In Proceedings of the 27th International Conference on Enterprise Information Systems (pp. 267-277). SCITEPRESS. Naguine, P., Arachchige, J. J., Bemthuis, R. H. & Bukhsh, F. A.https://doi.org/10.5220/0013289800003929

2024

Emergent Behaviors in a Resilient Logistics Supply Chain (2024)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Bemthuis, R. H.https://doi.org/10.3990/1.9789036560733A CRISP-DM-based Methodology for Assessing Agent-based Simulation Models using Process Mining (2024)[Working paper › Preprint]. ArXiv.org. Bemthuis, R. H., Govers, R. R. & Asadi, A.https://doi.org/10.48550/arXiv.2404.01114

Research profiles

My research interests include agent-based modeling, agent-based simulation, multi-agent systems, and process mining, focusing on, but not limited to, the application domains of transportation and logistics, healthcare, as well as construction sites and the built environment. 

Selection open bachelor thesis assignments (TCS, BIT and IEM): 

Process Mining

  • Synthetic Data Generation with LLMs for Process Mining
  • Distributed Process Mining
  • Assessing Risk in Process Models Using Fault Trees
  • Various Process Mining Applications

Agent-based Modeling and Simulation (or Multi Agent Systems)

  • Extraction of agent models using machine learning
  • Extraction of agent models using process mining
  • Multi agent system in domain-specific applications

(Co-)Supervision of Bachelor thesis projects (selection):

  • Dan Ploeşteanu (2025). Construction Vehicle Activity Detection in Low-Frequency Surveillance Imagery and Its Relationship to Local Air Quality. Study: TCS.
  • Guido Tiggelman (2025). An Approach for Supporting Temporary Power Technology Decisions in Construction Using Energy Forecasting Study: BIT. 
  • Ferdy Sloot (2025). Process mining and subpopulation comparison with the help of LLMs. Study: Create. 
  • Rodrigo Fernández Castillo (ongoing, expected: 2025). Study: TCS. 
  • Victor Cebotar (2025). Iterative Tactical Optimisation in Football Simulations through Process Mining and Agent-Based Modelling. Study: BIT. 
  • Ivan Mikheev (2025). A way of mapping the informational needs of stakeholders across financial institutions to the process model artefacts. Study: CreaTe. 
  • Zsombor Iványi (2025). Process Prediction from Event Logs by Machine Learning. Study: TCS. 
  • Byeonghun Park (2025). Comparative Study of Trace Clustering and Process Cube Sequences in Process Mining. Study: BIT. 
  • Patrick van Oerle (2025). Input Data Reduction on Natural Language Explanations of Business Processes using Large Language Models. Study: TCS. 
  • Allard van der Hooft (2024). Development of an intelligent multi-sensor system for real-time monitoring and analysis of soil pollution on construction sites. Study: CreaTe. 
  • Erjan Steenbergen (2024). Determining Case Identifiers in Event Logs from Hoists in Construction Sites to Estimate the Correlation between Usage Rates and BPMNs. Study: TCS. 
  • Fran Karlović (2024). Developing a Wearable Sensor Network for Air Pollution Monitoring on Construction Sites. Study: CreaTe. 
  • Hein Huijskes (2024). Effects of Variable Snapshot Frequency on Object Tracking. Study: TCS. 
  • Viktoriia Konashchuk (2024). Towards automated prevention of rework in software development. Study: BIT. 
  • Xander Küpers (2024). Idle Identification of Construction Machinery through a Deep Learning-Based Algorithm Embedded in Surveillance Camera Systems. Study: TCS. 
  • Victor Alecu (2024). Evaluation of Subpopulation Process Comparison Techniques for Process Mining. Study: TCS. 
  • Denise den Hartog (2022). Event record-based evaluation of business scenarios in the logistics domain using process mining. Study: BIT. 
  • Floor Rademaker (2022). Subpopulation process comparison for in-hospital treatment processes: a case study for sepsis treatment. Study: BIT. 
  • Tom Baas (2022). Creating standardized process mining applications based on the open trip model. Study: IEM. 
  • Venelina Pocheva (2022). Outsourcing Prioritization for Bottleneck Processes Using Process Mining: A Logistics Case Study. Study: TCS. 
  • Dennis Maneschijn (2021). Finding the Appropriate Level of Abstraction for Process Mining in Logistics. Study: BIT. 
  • Juri van Midden (2021). Using process mining and event log analysis for better business strategy decision-making. Study: BIT. 
  • Jennifer Cutina (2020). Assessing the use of process mining techniques to monitor the work process of commercial drivers. Study: BIT. 
  • Julian Stellaard (2020). Tracking the process of an outbreak to a pandemic via logistical infrastructures: case study. Study: TCS. 
  • Niels van Slooten (2020. A maturity level assessment of process mining bottleneck analysis techniques. Study: BIT. 
  • Simon Arends (2020). Quantity meets quality for mined process models through simulated events. Study: TCS. 

(Co-)Supervision of Master thesis projects (selection): 

  • Marijke Asgaart (ongoing, expected: 2026). Topic: predictive process monitoring. Study: IEM
  • Frank Ruempol (ongoing, expected: 2025). Topic: agents and LLMs. Study: BIT. 
  • Haritha Mutharasu (ongoing, expected: 2025). Enhancing Circular Economy in Consumer Electronics: Data-Driven Strategies for Improving E-Commerce Returns of Personal Care Electronics. Study: BIT. 
  • Stefan Kooy (ongoing, expected: 2025). Topic: LLMs and enterprise architecture. Study: BIT. 
  • Thomas Sepanosian (ongoing, expected: 2025). Topic: domain-specific modeling language. Study: BIT. 
  • Mohammad Alfayez (ongoing, expected: 2025). Topic: BIM-based plugin for sustainable renovatino. Study: M-CEM. 
  • Aletta Lohschelder (expected 2025). Optimising sustainable investments for CO2 reduction in construction: a decision-making model for Hegeman Holding. Study: IEM
  • Bas Vreeman (2025). Information Extraction From Sustainability Reports Using Document AI. Study: BIT. 
  • Stef Kosters (2024). Development of a Multi-Objective Resource-Constrained Scheduling Method for the Utility Construction Sector. Study: IEM. 
  • Ruben Govers (2023). An integrated process mining and data mining approach for the validation of agent-based simulation models. Study: BIT. 
  • Annemiek Fladderak (2022). Developing a planning and control policy for inventory cycle counting by UAVs. Study: IEM. 
  • Thijs Hammink (2022). Cycle counting with UAVs: Sample selection in time-restricted scenarios with neural network predictions. Study: IEM. 

External

  • Savita (Stockholm University)
  • Christin Pagels (Stockholm University). Leveraging Large Language Models for the Analysis and Quantification of Enterprise Architecture Debt. Supervision in collaboration with Simon Hacks. 

Supervision of Doctoral candidates

  • Fatemeh Massah (EngD, 2024-ongoing)
  • Egemen İşgüder (PhD, 2024-ongoing)
  • Arda Satici (EngD, 2025-ongoing)

Affiliated study programs

Courses academic year 2025/2026

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2024/2025

Courses academic year 2023/2024

Current projects

ECOLOGIC

Emission COntrol and Logistics Optimization for Green Infrastructure Construction

The research project ‘ECOLOGIC – Emission Control and Logistics Optimization for Green Infrastructure Construction’ aims to significantly improve the sustainability of the Dutch construction logistics sector by developing reliable data-driven insights and advanced analysis techniques (IoT and AI), through a real-time Carbon Digital Twin (DT), evaluated and demonstrated in two Living Labs. Through anticipatory and adaptive logistics planning, the emission footprint will be minimized while operations are optimized. The approach consists of the "Triple A": Acquire (collect and analyze data), Anticipate (predict), and Adapt (adjust to situations). The principle of federated data sharing is applied, where users only receive feedback on matters they can control themselves.

ECOCHECK

Environmental Construction Observation and Control using High-tech Equipment and Construction Knowledge

The construction industry is in a crisis akin to a "lockdown"?, with projects delayed and deadlines postponed due to the emission crisis. Growing public and regulatory pressures underscore the importance of sustainability in construction. ECOCHECK uses intelligent sensors to unlock real-time data on energy consumption and noise levels at construction sites. This data-driven approach not only optimizes energy use and reduces noise pollution, but also holds the key to lifting the industry out of its current standstill. By checking what happens in reality, rather than relying solely on theoretical models, ECOCHECK ensures we stay on top of these pressing issues.

Finished projects

DataRel

Big Data for Resilient Logistics

The modern supply chain continues to seek more cost savings and greater transparency and efficiency in all processes across the whole chain. The Internet of Things (IoT) is fundamentally transforming the transport industry and logistics. Next- generation smart logistics will optimize the movement of people and goods locally and globally, improving economics, public safety, and sustainability. This requires a multi-tiered intelligent system architecture providing high degree of modularity and autonomy, satisfying demanding and sometimes conflicting requirements of smart logistics and transport in terms of scalability, availability, and security. The wide availability of sensors (e.g., smart packaging, sensing technology, temperature control, board computers, GPS trackers, etc.) in logistics supply chains and the wide-band communication of Internet of Things (IoT) allow the collection of Big Data from sensors placed at key points in the logistic supply chain. This project is a collaboration between several companies and universities, and partially funded by NWO. Within this project we are focusing on the following areas: - Logistics Internet of Things (Pervasive Systems Group) This PhD research will focus on a new approach to logistic processes involving cyber-physical networks of logistic entities (e.g., goods, vehicles, infrastructure) that embed smart sensors and business logic. These entities, called Smart Returnable Transport Items (SRTIs), are capable of collecting data from extant IoT devices and social networks. The main goal is to detect unexpected behavior and do adaptive sampling of data generated by collaborating SRTIs in the region of interest, in order to solve problems such as, lost and damaged perishable goods during transportation, waste reduction, handling, and storage throughout the lifecycle of products based on sensing and location capabilities, etc. - IoT-driven resilient multimodal planning in smart logistics (Industrial Engineering and Business Information Systems Group) This PhD research will focus on a new approach to use this data input provided by cyber-physical networks of logistic entities (e.g., goods, vehicles, infrastructure) that embed IoT devices in the real-time multi-modal planning of orders. More concretely, the main goal is to improve the resilience and performance of planning decisions in terms of sustainability, efficiency, queue delay, costs and quality, by developing Artificial Logistics Intelligence (ALI) capable of detecting and dealing with emergent phenomena (given large volumes of heterogeneous data) and capable of learning from the experience of human planners using machine learning.

Address

University of Twente

Zilverling (building no. 11), room 5010
Hallenweg 19
7522 NH Enschede
Netherlands

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