Abstract

Recently the world isfacing many unprecedented disruptions, such as extreme natural disasters and pandemics. In many realistic situations, the uncertainty may not occur frequently but canbring significant changes to system parameters. For example, the wildfire inCalifornia has caused damage to the power systems. A conventional sequentialdecision problem, where uncertainty takes place in every time period withrandom magnitude, does not model this uncommon type of event with the bestcomputational efficiency. We propose a new mathematical model, stochasticdisruptions, of which both the timing and the magnitude are random. We presenttwo concrete examples of stochastic disruptions applied in differentengineering applications. In the first example we discuss an optimal power flowproblem for a distribution electricity network, with generators and lines potentiallyfailing due to disruptions. In the second example we minimize the project spanwith a limited budget for acceleration, where the disruption can significantlychange the duration of some activities. We also discuss potential extensions tothe modeling framework on how we can construct a data-driven distributionallyrobust model for the timing randomness and utilize rare event simulation.

Time

12月29日（星期二）9:30-11:30

Speaker

Dr. HaoxiangYang is Postdoctoral Research Associate ofthe Center for Nonlinear Studies at Los Alamos National Laboratory.  Hegraduated with a PhD degree from the Department of Industrial Engineering andManagement Sciences at Northwestern University.  His research focuses onthe theory of optimization under uncertainty, specifically in stochasticprogramming and robust optimization, integer programming and nonlinearprogramming, with its applications in energy systems, supply chain andlogistics, disaster relief and sports analytics.

Venue

Zoom会议室ID:91545731762

密码：350780