Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization

Abstract

The maintenance scheduling problem of a fleet of fighter aircraft is considered through multi-objective simulationoptimization (MOSO). In the problem, a maintenance schedule consisting of target starting times of the maintenance activities of the aircraft is determined. The objectives are to minimize average deviation between the target and actual starting times of the activities and to maximize average aircraft availability. The objectives depend on the maintenance schedule through complex interactions due to a policy in which the need for maintenance is based on the flight hours of the aircraft cumulated during flight missions. In addition, the durations of the flight missions, maintenance activities, and failure repairs are uncertain. Therefore, an MOSO approach is applied to the problem. The approach includes a discrete-event simulation model and a state-of-the art multi-objective simulated annealing algorithm for determining non-dominated schedules. Moreover, a multi-attribute value (MAV) function is used for supporting a maintenance decision-maker (DM) in selecting the preferred non-dominated schedule for implementation. The MAV function captures incomplete information on the values of the objectives as well as on the DM's preference statements regarding the weights of the objectives. The approach is implemented as an MOSO tool whereby the DM can consider the complex interactions and uncertainties of the problem which have not been addressed in the existing literature on maintenance scheduling. The approach and the tool are illustrated with a set of test problems as well as a real-life example problem.

Simulation Optimisation for Operating Room Scheduling

“…In simulation optimisation, simulated annealing has been used as search engine in some cases. A multiobjective optimisation approach based on simulated annealing and discrete event simulation was presented by [23] for the maintenance scheduling problem of a fleet of fighter aircraft. Simulation optimisation of police patrol districting plans is studied by [24].…”

“…Based on the mean values, the ranking is SA, SPT, LPT, FIFO. To determine a ranking with significant difference among the four heuristics, it is necessary to perform an allpairwise comparison [23]. This approach compares all the possible pairs and constructs confidence intervals for the mean difference.…”

Testing aircraft fleet management policies using simulation experiments

“…Most military aircraft fleet management work in the literature seeks to examine short-term planning and scheduling problems, using techniques such as mixed-integer linear programming (e.g., Hahn & Alexander, 2008;Kozanidis et al, 2012) or simulation-optimisation (e.g., Mattila & Virtanen, 2014). The aims of this work are: 1) to test various fleet management policies over an entire fleet life (in this case, for the incoming MH-60R fleet) to ascertain which are the most robust to a range of circumstances; and 2) to determine which model input values have the most influence on the results.…”

A review of asset management literature on multi-asset systems

“…This configuration allows the user to estimate the level of spare machine inventory that minimises the downtime cost. Other studies that consider this relationship are[97] and[98].…”