Data driven decision making in e-commerce
- PASS: Programmatic Advertising Support System, funded by NWO, 2017 – 2018
- PADS: Programmatic Advertising Decision System, funded by EU, EUROSTARS, 2017 – 2020
Revenue optimization in Internet advertising is very challenging due to the highly dynamic environment. The future supply, i.e., available website slots to display advertisements, and the demand of advertisement owners, are largely uncertain in the online market. In this line of research, we aim to develop a decision-support tool for revenue optimization for online companies who provide content (e.g., website slots for advertisements) or physical goods (e.g., online auctioneers), companies whose major revenues are collected from making sales to customers on the Internet. We will combine state-of-the-art techniques from Artificial Intelligence and Optimization.
Data driven decision making in logistics
- LARA: Lane Analysis and Route Advisor, funded by TKI Dinalog, 2019-2021
We will conduct research into solutions based on advanced data analytics that combine the integration of various data sources (‘big data’), AI methods such as machine learning (ML), natural language processing (NLP), semantic web techniques, and optimization techniques for prescriptive analytics and decision making. These methods will be applied to the optimization of route planning in global freight forwarding, initially targeted at air freight shipment with special handling needs.
- DARA: Data Analytics for Trade Lane Risk Assessments and Control, funded by TKI Dinalog, 2017 – 2019
The project will conduct research into innovative solutions to optimize trade lane risk management for global pharmaceutical logistics. Together with our industrial partners, we aim to develop advanced data analytics solutions that can significantly improve current risk assessments and result in further trade lane optimization.
- Real-time data-driven maintenance logistics, funded by NWO, 2017 – 2022
Companies in maintenance logistics aspire to transition from traditional static maintenance logistics plans based on rigid task intervals to dynamic maintenance logistics policies fuelled by real-time data. This project aims at developing efficient algorithms and analytical tools that enable companies in the service logistic sector to better plan and control dynamic planning. The developed ICT prototype integrates real-time data from smart assets with maintenance planning, while providing support to the human operator, when dealing with complex decisions based on huge streams of heterogeneous data.