ESS: Scheduling, Inventory Optimization, and Coordination of Maintenance Networks

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Many industries rely on the proper functioning of their key assets, such as airlines on jet engines, electric utilities on generators, and army on helicopters. A major obstacle on operation efficiency is asset unavailability due to forced outages and regular maintenance operations. The latter, although planned, are generally characterized by long and uncertain durations. Instead of embarking on more intensive preventative maintenance programs, the trend is to monitor asset conditions, and perform maintenance operations based on conditions to increase asset availability and reliability. This imposes major challenges on the networks of maintenance service providers, including overhaul shops, repair shops, part distributors, and spare part manufacturers, to have short and predictable turn-around-times while reducing inventory in systems traditionally characterized by massive uncertainties.The research is on scheduling, inventory optimization, and coordination of maintenance service networks under dynamic and uncertain environments to meet the challenges of condition-based maintenance. By closely collaborating with our industrial partners United Technologies Research Center and i2 Technologies, Inc., three tasks will be performed. The first is to develop stochastic scheduling models for large-scale maintenance service networks based on a realistic discrete event simulation model provided by our industrial partners. A solution methodology that synergistically combines Lagrangian relaxation, stochastic optimization, and simulation will be developed to provide near-optimal solutions with quantifiable quality and confidence. The second task is to optimize part inventory, balancing inventory costs and part availability for the scheduling method developed in Task 1. Continuous and periodic review policies will be examined, and ordinal optimization will be used to optimize policy parameters. In the third task, the autonomous nature of various organizations within a maintenance service network will be investigated. The models of Tasks 1 and 2 will be decentralized. A distributed and asynchronous coordination mechanism will be established, building on the pricing concept that is consistent with the methods developed in Task 1. A mobile multi-agent system will then be designed and implemented for Internet-based deployment of the methods, paving the way for the next generation e-maintenance service networks. Our goal is to reap the full benefit of the new maintenance service paradigm by having short and predictable turn-around-times while reducing inventory levels, making the best use of asset condition information and the information technology infrastructure. The research shall also significantly contribute to the fundamental theory and practice of scheduling, inventory management, and supply chain management, improving the competitiveness and reliability of service enterprises.