Real-Time Dispatch Optimization
A hand-built Streamlit app running a real-time mixed-integer-programming optimizer that routes 200+ field technicians from live telemetry and work-scheduling data — pilot results projected an emergency-SLA lift from ~50% to 67%.
Operations Research MIP Python Optimization
The challenge
Emergency response across a multi-state gas utility depends on getting the right technician to the right place fast — across 200+ field techs and constantly shifting conditions.
The system
A hand-built Streamlit application running a real-time mixed-integer-programming (MIP) optimizer — integrating vehicle telemetry, customer-information data, and work-scheduling data, with ball-tree spatial algorithms for fast nearest-resource matching.
Outcome
Pilot results showed it would move emergency SLA from ~50% to 67%.