Thursday June 11th 2020


  • Frits Spieksma – Eindhoven University of Technology
  • Dick den Hertog – Tilburg University

Robust Optimization (RO) aims to generate solutions to optimization problems that are good for a wide range of realizations of parameter values. On one hand, RO has proven to be a very fruitful methodology allowing to protect a decision maker against uncertainty. On the other hand, there are still many fields where the use of advanced RO techniques may have a positive impact on the stability of solutions used. In this track, different aspects of Robust Optimization, both theoretical and applied, will be discussed; we trust that ensuing discussion will further advance the field, and hope to meet you at the symposium!


Dimitris Bertsimas, Massachusetts Institute of Technology

Title: t.b.a

Abstract: t.b.a.

Bart Smeulders, Eindhoven University of Technology

Handling uncertainty in Kidney Exchange Problems

Mathematical optimization techniques have established themselves as an important and indispensable tool in guiding decisions in kidney exchange programs. Current formulations and implementations can easily handle deterministic instances of even the largest KEPs. However, there are many sources of uncertainty in kidney exchange. These range from withdrawal of donors to medical incompatibilities revealed after matching and even scheduling conflicts between transplant centres. As a result, a large number of potential transplants identified in the matching fail. For example, in the NHS Living Kidney Sharing Scheme, 30% of identified matches did not proceed to transplant between 2013-2017. The American UNOS program at one point saw a 93% failure rate.

Recent research has focused on handling match failures in kidney exchange. A variety of recourse options, some of which are employed by functioning kidney exchanges, are being studied. In this talk, we look at some of the computational challenges encountered when incorporating uncertainty into kidney exchange.

Sebastian Stiller, TU Braunschweig

Robust Optimization: From timetables to machine learning

Abstract: Robust optimization is an approach to stochastic optimization without stochasticity. In this talk we will take a tour through the development of robust optimization from its basic ideas and results over the challenges and solutions in two-stage robustness until recent results showing the advantages of using robust optimization for reliable machine learning and explainable AI.

Aurélie C. Thiele, Southern Methodist University

Robust optimization with societal applications 

Abstract: I will present both strategic and tactical decision making models under high uncertainty with a focus on the sequential optimization problems that arise in societal applications such as wildfire management. 2018 was the costliest wildfire season on record in the United States, with 2017 being the second costliest, which suggests the need for novel, more effective models of resource allocation. The uncertainty is on fire occurrence and fire growth characteristics, which depend on exogenous resources such as weather conditions as well as exogenous resources used to mitigate or suppress the fire. Strategic decisions include resource allocation and deployment and location set covering problems.  Tactical decisions include resource re-allocation due to fluctuating fire load from initial attach dispatching to extended attack management. We will show how adaptive and adjustable robust optimization models can provide insightful solutions in problem settings that are difficult to solve with the traditional method of stochastic programming. The robust solutions perform well in simulations and are easy to explain to firefighting personnel. Time permitting we will discuss the extent of which this framework can be extended to health epidemics.   

Wolfram Wiesemann, Imperial College London

Distributionally Robust Vehicle Routing

Abstract: We study variants of the capacitated vehicle routing problem (CVRP), which asks for the cost-optimal delivery of a single product to geographically dispersed customers through a fleet of capacity-constrained vehicles. Contrary to the classical CVRP, which assumes that the customer demands are deterministic, we model the demands as a random vector whose distribution is only known to belong to an ambiguity set. We then seek to determine delivery schedules that faithfully account for the risk and uncertainty preferences of the decision maker. We argue that the emerging distributionally robust CVRPs can be solved efficiently with standard branch-and-cut algorithms under suitable conditions. Our numerical results indicate that the distributionally robust CVRP has favorable scaling properties and can often be solved in runtimes comparable to those of the deterministic CVRP.