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Optimizing the maintenance schedule for a vehicle fleet: a simulation based case study

Yali Wang, Steffen Limmer, Duc Nguyen, Markus Olhofer, Thomas Bäck, Michael Emmerich, "Optimizing the maintenance schedule for a vehicle fleet: a simulation based case study", Engineering Optimization, 2021.


Vehicle fleets, including cars, trucks, buses, support a diverse array of functions in the world of today. The rapid increase of vehicle fleets in the global transport sector has been seen in recent years. For a vehicle fleet, the maintenance plays a critical role, a good maintenance schedule can reduce related expenses, increase the efficiency of the assets, ensure consistent service delivery, and even reduce its carbon footprint. In this work, we propose an evolutionary algorithm to optimize the vehicle fleet maintenance schedule based on the predicted remaining useful life (RUL) of vehicle components and the conditions in workshops to reduce the costs of repairs, keep vehicles stay on the road longer and make them safer for drivers. The proposed multi-objective evolutionary algorithm (MOEA) is further enhanced to precisely focus on the preferred solutions. When the maintenance schedule is updated periodically, we involve the stability as another objective in our dynamic MOEA to handle the problem under the environment changes. To implement the complete process of vehicle fleet maintenance scheduling optimization, we develop a simulator which can define vehicles, workshops, simulate driving tasks, predict the components’ RUL and optimize the maintenance schedule in a rolling-horizon fashion. Various scenarios can be simulated through the simulator, the results of our proposed MOEA and preference based MOEA under different scenarios have been reported and compared.

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