10h30 - 10h55
Dynamic emergency medical services network design: A novel probabilistic envelope constrained stochastic model
This talk introduces a two-stage stochastic programming model for dynamic emergency medical services network design. This model enforces a minimum probabilistic coverage (either through chance constraints or probabilistic envelope constraints) of future emergency demand while minimizing total expected cost over a planning horizon. Numerical experiments involve data from Northern Ireland.
10h55 - 11h20
Improving stroke routing protocol
Stroke is medical emergency and must be treated immediately. However, transporting patients to the closest stroke hospital may not be the best solution. This often causes congestion in some hospitals, while underutilization in others. We study patients routing protocol under congestion using robust queuing technique.
11h20 - 11h45
Robust binary optimization with an application to talent analytics
We consider a binary linear programming problem in the presence of high uncertainty on the objective coefficients. We investigate theoretically four models based on an "estimate-then-optimize" paradigm and a robust optimization approach. We finally compare the results of those four methods using real-life data about baseball team player selection.
11h45 - 12h10
Preference robust optimization for decision making under uncertainty
While different risk measures can account for risk aversion, it is often unclear which one models best a decision maker’s perception of risk. We introduce preference robust optimization as a way of accounting for ambiguity about the DM’s preferences. We illustrate numerically our findings with a portfolio allocation problem and discuss possible extensions.