We are looking for a Ph.D. candidate with a background in industrial engineering, operations research, mathematics, econometrics, operations management, or computer science. Candidates with an affinity for AI are also encouraged to apply. The candidate should be interested in fundamental research with practical and societal relevance in the area of Urban Mobility and Logistics, and transportation and logistics in general.
Today, roughly 54% of the world's population lives in cities. The UN estimates that urbanization will continue, resulting in 68% of the world's population living in urban areas by 2050. Optimization of urban mobility and logistics services is key to counteract increased congestion and pollution. These urban mobility and logistics solutions include last-mile delivery (e.g., autonomous vehicles, parcel lockers, cargo bikes) and shared mobility and logistics concepts (e.g. Uber, Lyft, crowdsourced deliveries, meal deliveries).
This project investigates how future city logistics networks should be shaped. It should allow for sustainable and fruitful collaboration of existing stakeholders, accommodate technological developments, and place the customer at the center of attention. We stimulate developing your own research line, but example sub-projects might cover:
- The impact of technology choice (electric vehicles, bike couriers) on hub location and design.
- Dynamic routing of multi-purpose (freight, parcels, humans) same-day delivery services, and its impact on city hub network design.
- Network design at the interface of static and dynamic decision making
- Collaborative vehicle routing and driver coordination
These challenges will be addressed from a transportation science and operations management vantage point. The goal of this project is to develop mathematical models, methods, and algorithms to address these challenges. To bridge the gap between operational and strategic aspects, this project potentially employs mixed-integer optimization, approximate dynamic programming, novel approximations, heuristics, and decomposition techniques. In extensions, this project may also leverage quadratic programming, game theory, and data science.
You, as a successful applicant, will perform the Ph.D. project outlined above. The research will be concluded with a Ph.D. thesis. You will be supervised by dr. Layla Martin, dr. Albert Schrotenboer and prof. dr. ir. Tom van Woensel. A small teaching load is part of the job.
For details and to apply, please refer to PhD position on optimization of urban mobility and logistics concepts