Research
Modeling Stochastic Disruptions and Solution Algorithms
Modeling Stochastic Disruptions and Solution Algorithms
In real life, many uncertain events may be modeled as stochastic disruptions, such as natural disasters, pandemics, and man-made mistakes. In addition to the magnitude of disruptions, we also need to model their temporal dynamics as the timing of such disruptions matters. We can categorize the problem according to their time horizon:
- Continuous time horizon:
- Discrete time horizon:
- N-1 contingency in power systems
- Distributionally robust optimization with stochastic disruptions
Stochastic Disruptions in Complex Systems
Stochastic Disruptions in Complex Systems
Applications of the stochastic disruption concept are everywhere in real-life complex systems. Some examples of my research in this aspect are listed here:
- Transportation systems: diesel fuel supply chain optimization after hurricanes
- Infrastructure systems: robust optimization in power generation
- Infrastructure systems: interdependent network hardening/repair decisions
- Healthcare systems: needle exchange program clients behavior change modeling
- Healthcare systems: COVID-19 tier lockdown strategy