  A preference based multi-objective evolutionary algorithm is proposed for
generating solutions in an automatically detected knee point region. It is
named Automatic Preference based DI-MOEA (AP-DI-MOEA) where DI-MOEA stands for
Diversity-Indicator based Multi-Objective Evolutionary Algorithm). AP-DI-MOEA
has two main characteristics: firstly, it generates the preference region
automatically during the optimization; secondly, it concentrates the solution
set in this preference region. Moreover, the real-world vehicle fleet
maintenance scheduling optimization (VFMSO) problem is formulated, and a
customized multi-objective evolutionary algorithm (MOEA) is proposed to
optimize maintenance schedules of vehicle fleets based on the predicted failure
distribution of the components of cars. Furthermore, the customized MOEA for
VFMSO is combined with AP-DI-MOEA to find maintenance schedules in the
automatically generated preference region. Experimental results on
multi-objective benchmark problems and our three-objective real-world
application problems show that the newly proposed algorithm can generate the
preference region accurately and that it can obtain better solutions in the
preference region. Especially, in many cases, under the same budget, the Pareto
optimal solutions obtained by AP-DI-MOEA dominate solutions obtained by MOEAs
that pursue the entire Pareto front.
