Integrated Production & Maintenance Optimization
Optimization models and algorithms that integrate production scheduling and preventive maintenance under uncertainty to improve system-level performance.
Problem Scope
In most industrial systems, production and maintenance are optimized separately. Production focuses on throughput and deadlines; maintenance focuses on failure prevention and downtime reduction. When treated independently, the overall system becomes suboptimal.
The real problem is not scheduling jobs or scheduling maintenance — it is designing an integrated decision system that balances availability, cost, reliability, and uncertainty simultaneously.
Approach
This body of work develops mathematical programming models that:
- Integrate preventive maintenance into production schedules
- Incorporate stochastic failures and horizon uncertainty
- Consider multi-objective tradeoffs (cost, tardiness, availability)
- Provide efficient solution methods for large-scale instances
The research spans exact algorithms, stochastic modeling, and simulation–optimization approaches.
Impact
These models demonstrate how coordinated planning improves system availability and reduces long-term cost. They form the theoretical foundation for decision automation in maintenance-intensive industries such as manufacturing, aviation, and infrastructure systems.
Selected Publications
- Seif & Yu (2018), Computers & Operations Research
- Seif et al. (2019), Flexible Services and Manufacturing Journal
- Seif et al. (2018), International Journal of Production Research
- Yu & Seif (2016), Computers & Industrial Engineering