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 focuses on enhancing logistics knowledge through innovative Internet of Things (IoT) and Big Data solutions to detect emerging behaviors. He was a visiting scholar at the University of Southern Denmark (SDU) and the Karlsruhe Institute of Technology (KIT). Currently, he is coordinating and conducting research within the ECOLOGIC project, which focuses on sustainable construction logistics. His research interests include multi-agent systems (MAS), simulation of logistic processes, and applied process mining, with a special emphasis on IoT-based solutions. 

Besides his research activities, Rob is an active volunteer in the international community of the University of Twente and Enschede. He served as Treasurer of the PhD Network of the University of Twente (P-NUT) in 2019-2020, held a general board member position in 2020-2021, and was elected Treasurer and Vice-President in 2021-2022. He also chaired the organizing committees of the PhD Day 2019, PhD & PDEng Day 2020, and PhD & PDEng Day 2021 at the University of Twente.

Expertise

  • Computer Science

    • Process Mining
    • Models
    • Case Study
    • Process Mining Technique
    • Process Model
    • Simulation Mode
    • Events
    • Design

Organisations

Publications

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.2404.01114Assessing Factory’s Industry 4.0 Readiness: A Practical Method for IIoT Sensor and Network Analysis (2024)Procedia computer science, 232, 2730-2739. Anbalagan, S. N., Schwarz, M., Bemthuis, R. & Havinga, P. J. M.https://doi.org/10.1016/j.procs.2024.02.090Analyzing Sepsis Treatment Variations in Subpopulations with Process Mining (2024)In Proceedings of the 26th International Conference on Enterprise Information Systems (pp. 85-94) (International Conference on Enterprise Information Systems, ICEIS - Proceedings; Vol. 1). SCITEPRESS. Rademaker, F. M., Bemthuis, R. H., Jayasinghe, J. & Bukhsh, F. A.https://doi.org/10.5220/0012600700003690Exploring the Integration of Agent-Based Modelling, Process Mining, and Business Process Management through a Text Analytics–Based Literature Review (2024)In The Oxford Handbook of Agent-based Computational Management Science. Oxford University Press. Bukhsh, F. A., Govers, R., Bemthuis, R. H. & Iacob, M. E.https://doi.org/10.1093/oxfordhb/9780197668122.013.20Using Process Mining for Face Validity Assessment in Agent-Based Simulation Models: An Exploratory Case Study (2024)In Cooperative Information Systems: 29th International Conference, CoopIS 2023, Groningen, The Netherlands, October 30–November 3, 2023, Proceedings (pp. 311-326) (Lecture Notes in Computer Science; Vol. 14353). Springer Nature. Bemthuis, R. H., Govers, R. & Lazarova-Molnar, S.https://doi.org/10.1007/978-3-031-46846-9_17

2023

An Approach for Face Validity Assessment of Agent-Based Simulation Models Through Outlier Detection with Process Mining (2023)In Enterprise Design, Operations, and Computing: 27th International Conference, EDOC 2023, Groningen, The Netherlands, October 30 – November 3, 2023, Proceedings (pp. 134-151) (Lecture Notes in Computer Science; Vol. 14367). Springer Nature. Bemthuis, R. H. & Lazarova-Molnar, S.https://doi.org/10.1007/978-3-031-46587-1_8A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques (2023)In E-Business and Telecommunications: 18th International Conference on E-Business and Telecommunications, ICETE 2021, Virtual Event, July 6–9, 2021, Revised Selected Papers (pp. 118-136) (Communications in Computer and Information Science (CCIS); Vol. 1795). Springer Nature. Piest, J. P. S., Bemthuis, R. H., Cutinha, J. A., Arachchige, J. J. & Bukhsh, F. A.https://doi.org/10.1007/978-3-031-36840-0_7Using process mining for workarounds analysis in context: Learning from a small and medium-sized company case (2023)International Journal of Information Management Data Insights, 3(1). Article 100163. Wijnhoven, F., Hoffmann, P., Bemthuis, R. & Boksbeld, J.https://doi.org/10.1016/j.jjimei.2023.100163Balancing Simplicity and Complexity in Modeling Mined Business Processes: A User Perspective (2023)In Enterprise Information Systems. ICEIS 2022 (pp. 3-21) (Lecture Notes in Business Information Processing; Vol. 487). Springer Nature. Maneschijn, D. G. J. C., Bemthuis, R. H., Arachchige, J. J., Bukhsh, F. A. & Iacob, M. E.https://doi.org/10.1007/978-3-031-39386-0_1

Research profiles

Are you a student interested in a graduation project? I currently have a couple of open assignments related to research projects on Smart Construction Sites [link], and there are continuous assignments available within the ECOLOGIC research project [link]. You can also send me a proposal of a few lines indicating your research ideas, and we can further discuss if I could supervise you. Some graduation projects have lead to scientific publications. 

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 or BIT) for the year 2024-2025: 

Process Mining

  • Process Mining and Subpopulation Comparison
  • Synthetic Data Generation with LLMs for Process Mining
  • Design and Development of a Process Mining Thesis Project Tool
  • Exploring Celonis and its academic capabilities, helping conduct meaningful research in Process mining
  • Distributed Process Mining

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

Below is a selection of individual thesis assignments I have (co-)supervised. 

Supervision of Bachelor thesis projects (selection):

  • 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: CS. 
  • 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: CS. 
  • 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: CS. 
  • 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: CS. 
  • 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: CS. 
  • 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: CS. 

Supervision of Master thesis projects (selection): 

  • 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. 

Supervision of Doctoral candidates

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

Affiliated study programs

Courses academic year 2024/2025

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 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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