Optimize Maintenance Scheduling Under Real-World Constraints
Challenge:
"Our maintenance plans never survive contact with reality."
Solution:
We apply formal optimization to create schedules that are both optimal and executable.
Case Study: Integrated Oil & Gas Producer – Compressor Optimization
Developed an integrated model linking compressor configuration, maintenance windows, crew availability, and spare parts logistics to minimize downtime risk while maximizing production uptime. The model generates robust, executable schedules that account for real-world disruptions and constraints.
Behind the Scenes: Technical Approach
Method
Mixed-Integer Programming (MIP) + Heuristic Search
Tools
CPLEX, custom solvers in C++
Constraints Modeled
Weather delays, shift rotations, vendor lead times
Outcome
Generated robust, executable schedules validated against historical disruption patterns
Why This Approach Works
Real-World Constraints Built In
Unlike generic scheduling tools, we model crew certifications, tool availability, weather windows, and supply chain lead times — the factors that actually break theoretical schedules.
Optimization Under Uncertainty
Using Mixed-Integer Programming and stochastic modeling, we find schedules that remain feasible even when disruptions occur — not just optimal on paper.
Validated Against History
Every schedule is tested against years of historical disruption data to ensure resilience under real operating conditions.