Headshot of Shraddha Rana

Shraddha Rana is a PhD candidate in the Interdepartmental Transportation Program at MIT and a Graduate Research Assistant in the MIT FreightLab. Her research is focused on modeling the effects of unplanned disruptions in freight transportation as well as creating frameworks of mitigation strategies for government agencies and stakeholders.

We spoke with Shraddha about her forthcoming paper, “Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Downstream Fuel Distribution”, which evaluates how the flow fuel in the downstream distribution network can be increased to avoid shortages in times of distress, such as in the aftermath of disasters.

Why is it important to focus on fuel delivery during disasters?

Fuel is an essential resource, especially during disasters. Fuel is required for powering backup generators during grid outages, hauling relief supplies and emergency responders, as well as evacuation activities. Additionally, news of disaster further increases the demand due to panic buying and hoarding in anticipation of supply shortages. Thus, it is important to ensure adequate fuel availability for end-users during disasters.

What kind of disasters can impact fuel distribution?  

Disruptive events like natural disasters, leakage accidents, cyber-attacks etc. can impact fuel distribution. Damage to infrastructure or system shut down due to security threat hinders the physical capacity. Moreover, as fuel terminal employees and tanker truck drivers focus on surviving the disaster, operational capacity becomes limited. Effects can be felt at both upstream (ports, refineries, pipelines etc.) as well as downstream (fuel terminals, retail gas stations) distribution networks.

Which emergency policies does your work recommend? 

We recommend that policies that improve terminal gate process time should be applied to terminals with larger bay to gate ratios and shorter station distances. Similarly, bay process time improvement should be prioritized for terminals with smaller bay to gate ratios and shorter station distances. Policies that improve travel speed should be prioritized for terminals with a larger number of bays and longer station distances. Finally, fleet size improvement should be prioritized for terminals with multiple gates, a large number of bays, and longer station distances.

What challenges did you face when building simulation models of fuel distribution systems?  

Building a simulation model that could be generalizable irrespective of geography was a big challenge. Another challenge was data collection for running numerical experiments and case studies. We borrow parameter values from previous reports and a field visit and assume uniform demand. However, better informed policy recommendation can be achieved knowing actual process times, terminal layouts, station demands, routing etc.

What is next in your research stream? 

We are working on extending this research to model where interventions should be applied strategically, given probability of disaster and surge demand and consequently how fuel flow should be assigned from terminals with slack capacity to stations with high demand. Additionally, we are also studying truckload transportation performance during disruptive events to inform better procurement for episodic use during emergencies.