Joint seminar with the Department of Logistics and Operations Management, HEC Montréal.

In this talk, we will explore the concept of a more data-driven approach to location problems. We will address key questions that arise in the application of location models, algorithm design, and solution strategies. These questions are particularly important when dealing with real-world location problems that have certain temporal contexts and are subject to uncertainty and additional side constraints: Which problem aspects should be represented in the model? What are the key cost drivers in the model to identify the right objective function(s)? Which parts of the solution are performance-critical? Which decisions are driven by data structures and remain stable across model variants? Which algorithms perform best for a certain location problem, and why? How can the algorithm be adapted to the data? Whenever possible, we will suggest quantitative measures to evaluate the aforementioned aspects. Several papers have addressed some of these questions, but to our knowledge, there has been limited systematic work on data-driven aspects of location science. In addition to providing a general overview and review of DDLS, we will present new results for some specific aspects: Data-Driven Interpretation of Solutions for Location Problems, Data-Driven Algorithm Design and Data-Informed Model Choice.