Real-time Planning and Optimization under Uncertainty in Logistics and Transportation

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Logistics and transportation related operations typically account for large parts of total project costs and are linked to major pollution emission. Both private and public sectors call for more efficiency and reduction of transportation to relieve traffic and pollution emission. While mathematical optimization has been among the most successful tools to provide efficient planning solutions for these domains, models have been traditionally based on forecasts of the problems' parameters. The ongoing increase of information technology capacity to capture and store data from different sources offer an opportunity to the Operations Research community to improve the performance of produced planning solutions in practice.***In this research program, I focus on two key applications in logistics and transportation, namely the redeployment in vehicle sharing systems, in particular bike sharing systems and the near-future scenario of autonomous (driver-less) taxis, as well as facility location with mobile facilities that cover customer demands of moving individuals. Those systems have a particularly high potential to relieve the above mentioned issues, as data is often available (e.g. in telecommunication networks). However, they tend to suffer from events that are difficult to forecast (e.g. weather conditions), making a reliable satisfaction of customer demands a constant challenge. I propose to investigate optimization models and solutions methods that provide solutions to realistically sized planning problems by taking into consideration the uncertainty in the (forecast) input data, external data that has typically not been considered in optimization models, as well as real-time information that is released throughout the execution process. We will tackle three main challenges: (i) the problems are combinatorial and, by nature of the applications, have to be solved on large scale and are therefore difficult to solve even when perfect information is available; (ii) the problems are dynamic and decisions have to be adjusted throughout the planning; (iii) the problems' input data is subject to uncertainty.***The models and solution methods developed in this research program may have a strong impact in logistics and transportation industries and provide guidance to decision-makers, e.g. by improving telecommunication services, increasing the utility of vehicle sharing systems and reducing traffic and vehicle-ownership. The program advances our understanding of how information uncertainty translates into optimal decisions, of how data from external sources can be useful to improve decision-making, and of the so-far little explored interplay between operations research and machine learning. Students will be trained as the next data scientist generation specialized in prescriptive analytics and will benefit from an excellent job market.