This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Optimize a Thru Flight Inspection of a Fighting Falcon using Routing Algorithms

Journal Article
2011-01-2750
ISSN: 1946-3855, e-ISSN: 1946-3901
Published October 18, 2011 by SAE International in United States
Optimize a Thru Flight Inspection of a Fighting Falcon using Routing Algorithms
Sector:
Citation: Lois, A., Bertos, N., and Ziliaskopoylos, A., "Optimize a Thru Flight Inspection of a Fighting Falcon using Routing Algorithms," SAE Int. J. Aerosp. 4(2):1380-1393, 2011, https://doi.org/10.4271/2011-01-2750.
Language: English

Abstract:

The process of checking inspection points on combat aircraft after a mission, is critical for their operational readiness. Manufacturers include specific inspection procedures in their maintenance handbooks. These procedures consist of detailed instructions for each check, the minimum time required to complete each check as well as a suggested sequence. However, it has been observed, that technical crews can complete inspection in less time than suggested by the manual, without violation of the time prescribed for each inspection point. In this work we will try to apply routing algorithms, to improve the total inspection time, by finding the optimal inspection sequence. This will be achieved without violating any constraint set by the manufacturer, except for the small reduction of the service time on some points. The algorithms we will use is the algorithmic set usually applied for the well-known PDPTW (pickup and delivery problem with time windows). Every inspection area is considered as a network G(N,A) consisting of nodes N, and arcs A. Every node (Inspection Point) is characterized by the Time Window, defined as the time interval earliest-latest [ei,li], during which the point should be inspected. It is also characterized by the Service Time si, defined as the time required to complete the check on this point. Every arc between two nodes describes the time cod, needed for the technician to move from the first node (Origin) to second node (Destination). These algorithms will be run to produce the optimal path (sequence) or the best attainable suboptimal path that minimizes the objective.
The data set consists of real data from inspections logs.