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

Abstract

Vehicle fleets support a diverse array of functions and are increasing rapidly in the world of today. For a vehicle fleet, maintenance plays a critical role. In this article, an evolutionary algorithm is proposed to optimize the vehicle fleet maintenance schedule based on the predicted remaining useful lifetime (RUL) of vehicle components to reduce the costs of repairs, decrease maintenance downtime and make them safer for drivers. The multi-objective evolutionary algorithm (MOEA) is then enhanced to focus precisely on the preferred solutions. Moreover, stability is involved as another objective in the dynamic MOEA for handling the problem under changes in the environment. To implement the complete maintenance process, a simulator is developed that can define vehicles, predict the RUL of components and optimize the maintenance schedule in a rolling-horizon fashion. The results of the proposed MOEAs under different scenarios are reported and compared.