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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

Publications