Vehicle Fleet Maintenance Scheduling Optimization by Multi-objective Evolutionary Algorithms

In this paper, a new real-world application problem, i.e., the vehicle fleet maintenance scheduling optimization problem, is defined and a specialized multi-objective evolutionary algorithm framework (grouping strategy, three vector chromosome and corresponding genetic operators) is proposed to solve the problem. State-of-the-art multi-objective evolutionary algorithms such as NSGA-III, SMS-EMOA, DI-MOEA are employed in the proposed algorithm framework to solve the problem, and their behavior is investigated. Although DI-MOEA is used the first time for a real-world application problem, its performance is better than other algorithms for some instances.