PLAN
Maintenance Optimization
Design optimal maintenance strategies balancing preventive, predictive, and corrective approaches while minimizing production disruption and ensuring equipment reliability.
Understanding the Problem
Unplanned downtime costs industrial manufacturers an estimated $50 billion annually. Maintenance optimization addresses this challenge by systematically coordinating maintenance activities across hundreds of interdependent components while balancing costs, reliability, and production continuity. The goal is to prevent expensive emergency repairs while avoiding unnecessary preventive maintenance.
THE CHALLENGE
What Makes it Hard
Organizations must coordinate maintenance schedules across hundreds of assets with varying failure patterns, resource requirements, and production constraints. The challenge involves balancing competing objectives (cost vs. reliability) under uncertainty from stochastic equipment failures and dynamic production schedules.
WHO FACES IT
Multi-objective trade-offs between maintenance costs (emergency repairs cost 3-5x planned maintenance) and equipment reliability
Complex scheduling across interdependent components with varying failure patterns and limited maintenance windows
Stochastic equipment failure rates, unpredictable maintenance durations, and dynamic production schedules create uncertainty
BUSINESS IMPACT
Predictive maintenance reduces costs by 18-40% and extends equipment life 20-40%, avoiding emergency repairs that cost 3-5x more than planned maintenance.
Shift from reactive repairs to planned maintenance windows.
Emergency Repairs
3-5x
Cost Multiplier[1]
Reactive maintenance costs 3-5 times more than planned maintenance (U.S. Department of Energy).
Equipment Lifespan
20-40%
Extension[2]
Predictive maintenance reduces machine downtime by 30-50% and increases machine life by 20-40% (McKinsey).
Cost Reduction
18-40%
Maintenance Savings[3]
Predictive maintenance delivers 18-40% reductions in overall maintenance costs compared to reactive approaches.
How We Solve It
Mixed-integer linear programming models optimize maintenance task assignments across time periods and resources. The formulation minimizes total costs (preventive + corrective + downtime) while satisfying resource availability, temporal dependencies, equipment reliability requirements, and production schedule constraints.
Hybrid Compute
What We Bring
Systematic coordination of maintenance across hundreds of interdependent components
Resource optimization balancing crew schedules, spare parts, and budget constraints
Integration with production schedules to minimize conflicts and downtime
Reliability-centered planning using MTBF and MTTR metrics
FUTURE POSSIBILITIES
The
Quantum Horizon
Quantum approaches target QUBO formulations of scheduling problems, but classical MILP solvers remain strongly preferred for production use.
Exploratory Work
Quantum annealing shows promise for specific scheduling subproblems, with some benchmarks claiming speedups over classical solvers. However, QUBO reformulation overhead, limited hardware capabilities, and lack of consistent quantum advantage make classical methods strongly preferred. Organizations should focus on robust MILP optimization with commercial solvers while monitoring quantum developments for potential future relevance.
Current Research Directions
Variational quantum algorithms (QAOA, VQE) for small maintenance scheduling instances
D-Wave quantum annealing demonstrations on railway maintenance scheduling
Hybrid quantum-classical frameworks combining quantum subroutines with classical optimization
Interested in quantum research?
Explore proof-of-concept implementations with our team.
Ready to solve this problem?
Talk to our experts about how Strangeworks can help with maintenance optimization.
SOURCES