We used discrete-event simulation to model the maintenance of fighter aircraft and improve maintenance-related decision making within the Finnish Air Force. We implemented the simulation model as a stand-alone tool that maintenance designers could use independently. The model has helped the designers to study the impact of maintenance resources, policies, and operating conditions on aircraft availability. It has also enabled the Finnish Air Force to advance the operational capability of its aircraft fleet. We designed the model to simulate both normal and conflict operating conditions. The main challenge of the project was the scarcity and confidentiality of data about the fighter aircraft, their maintenance, and various operational scenarios, especially during conflict situations.

This paper presents a new simulated annealing (SA) algorithm for noisy multiobjective optimization with continuous decision variables. A novel feature of the algorithm in the context of SA is that the performance of a candidate solution is determined by estimating the probabilities that the candidate is dominated by the current non‐dominated solutions. The sum of these probabilities provides a scalar performance measure that is used to determine the acceptance of the candidate as the current solution and whether the candidate is inserted into the non‐dominated set. The second novel feature of the algorithm is the technique utilized for generating candidate solutions. Empirical probability distributions for sampling the new values of the decision variables are constructed on the basis of the values of the variables in the current non‐dominated set. Thus, the information contained by the non‐dominated set is utilized to improve the quality of the generated candidates, whereas this information is ignored in the existing multiobjective SA algorithms. The proposed algorithm is compared with a reference state‐of‐the‐art evolutionary algorithm as well as two other SA algorithms in numerical experiments involving 16 problems from commonly applied test suites. The proposed algorithm performs as good or better compared with the reference algorithms in majority of the experiments and therefore represents a promising solution method for noisy multiobjective optimization problems. Copyright © 2012 John Wiley & Sons, Ltd.

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.

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