R.M. van Steenbergen MSc (Robert)

Robert van Steenbergen is a BMS signature PhD candidate at the Department of Industrial Engineering and Business Information Systems (IEBIS), and working together with the Department of Governance and Technology for Sustainable Development (CSTM). Before starting as a PhD researcher, Robert completed his bachelor and master in Industrial Engineering and Management at the University of Twente (both cum laude) and followed the MSc Research Honours Programme.

In his research, he investigates the application of Unmanned Aerial Vehicles (UAVs), or drones, to humanitarian logistics. After a severe disaster, affected people require relief goods (e.g., shelter, food, water). Considering damaged infrastructures, UAVs can offer an effective alternative to trucks for the distribution of relief goods. UAVs can also use remote sensing techniques to gather information about the uncertain and volatile disaster area. The objective is to analyze in which situations, in what ways and to what degree UAVs can contribute to humanitarian logistics, using simulation modelling, artificial intelligence and digital twin technology. Furthermore, the aim is build knowledge on effective deployment of UAVs, to be prepared when the next disaster occurs.

Engineering & Materials Science

# Disasters
# Logistics
# Sales
# Unmanned Aerial Vehicles (Uav)

Business & Economics

# Demand Forecasting
# Inventory Management
# New Products

Social Sciences

# Demand

Faculty of Behavioural, Management and Social Sciences (BMS), Industrial Engineering & Business Information Systems (IEBIS)

van Steenbergen, R. M. , Mes, M. , & van Heeswijk, W. J. A. (2023). Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty. Transportation Research Part C: Emerging Technologies, 157, Article 104401. https://doi.org/10.1016/j.trc.2023.104401

van Steenbergen, R. M. , Lalla-Ruiz, E. , van Heeswijk, W. J. A. , & Mes, M. (2023). The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. In J. R. Daduna, G. Liedtke, X. Shi, & S. Voß (Eds.), Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings (pp. 276-291). (Lecture Notes in Computer Science; Vol. 14239). Springer. https://doi.org/10.1007/978-3-031-43612-3_17

van Steenbergen, R. M., Brunetti, M. , & Mes, M. (2021). Network Generation for Simulation of Multimodal Logistics Systems. In 2021 Winter Simulation Conference (WSC) IEEE. https://doi.org/10.1109/WSC52266.2021.9715497

van Steenbergen, R. M. , & Mes, M. (2020). A Simulation Framework For UAV-Aided Humanitarian Logistics. 1372. Paper presented at Winter Simulation Conference, WSC 2020, Orlando, United States.

van Steenbergen, R. M. , & Mes, M. R. K. (2020). Forecasting demand profiles of new products. Decision support systems, 139, Article 113401. https://doi.org/10.1016/j.dss.2020.113401

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