Scheduling maintenance of electrical power transmission networks using genetic programming
Artificial intelligence techniques in power systems
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Intelligent prognostics tools and e-maintenance
Computers in Industry - Special issue: E-maintenance
Journal of Intelligent Manufacturing
Note to: Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm
Journal of Intelligent Manufacturing
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In this paper, a new approach to maintenance scheduling for a multi-component production system which takes into account the real-time information from workstations including remaining reliability of equipments as well as work-in-process inventories in each workstation is proposed. To model dynamics of the system, other information like production line configuration, cycle times, buffers' capacity and mean time to repair of machines are also considered. Using factorial experiment design the problem is formulated to comprehensively monitor the effects of each possible schedule on throughput of the production system. The optimal maintenance schedule is searched by genetic algorithm-based optimization engine implemented in a simulation optimization platform. The proposed approach exploits all of makespans of planning horizon to find the best opportunity to perform maintenance actions on degrading machines in a way that maximizes the system throughput and mitigates the production losses caused by imperfect traditional maintenance strategies. Finally the proposed method is tested in a real production line to magnify the accuracy of proposed scheduling method. The experimental results indicate that the proposed approach guarantees the operational productivity and scheduling efficiency as well.