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Making The Biggest Maintenance Decision: When To Stop

New genetic algorithms may help.

One of the most important maintenance decisions is when to stop doing maintenance on an aircraft. Usually that is because an aircraft or a whole fleet of a particular type will be retired. But should the retirement happen before or after a maintenance visit? When in fact should aircraft be retired? And how should stocks be sized as the fleet they support decline?

The Swedish companies Cervino and Systecon have joined forces and work on these sort of critical and complicated questions. And it has several different approaches. “We work with different tools and methodologies depending on the questions at hand,” notes managing director Fredrik Ekstrand.

For questions concerning actual retirement schedules, Cervino and Systecon have developed a method based on genetic algorithms. These algorithms mimic the process of natural selection and can be used to find practical solutions to very tough optimization and search challenges. Genetic algorithms are suitable in situations where an airline must search “a very large solution space for a specific solution that fulfills a specific requirement,” Ekstrand explains. For, example, which retirement plan or approach is most cost effective and does not compromise operations?

This genetic algorithms solver is still a research and development prototype and not yet a commercially offered software. But in a recent research case, genetic algorithms were applied to a phase-out scenario of an aircraft fleet, and the optimal stop-maintenance strategy and part-out strategy were determined for a set of rotables.

If the possible retirement solutions can be limited, for example by recognizing certain decisions already made about how and when retirements should be conducted, Cervino can use its Simlox tool, a simulation module in Systecon’s Opus Suite of software. “The answer the owner of the aircraft is looking for is when is the best time to retire the fleet, such as before or after a scheduled maintenance?” Ekstrand says. “This is one of the key questions we try to help find an answer to. By using all data such as maintenance cost, rotable cost and all relevant agreements, we can simulate different scenarios to help the decision.”

The Opus Suite also includes tools for optimizing inventory levels, at the acquisition of a fleet, during its regular operation and as the fleet is being drawn down by retirement. In one recent optimization of rotables, Cervino found an airline could decrease the cost of its rotables inventory by 25% and still ensure the same service level. Following that case, Cervino and Systecon optimized inventories for two different fleets, one phasing in and one phasing out. “By actively working with inventory optimization during ramp up and retirement we could cut the costs of the inventories by an additional 25% while maintaining the same service level,” Ekstrand says.

Although the new genetics algorithms for answering the most difficult retirement questions are still under development, the other tools in the Opus Suite are used by more than 1,000 licensees worldwide, including Boeing, Airbus, Lockheed Martin and western air forces.

TAGS: Software
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