  Learning-based heuristics for solving combinatorial optimization problems has
recently attracted much academic attention. While most of the existing works
only consider the single objective problem with simple constraints, many
real-world problems have the multiobjective perspective and contain a rich set
of constraints. This paper proposes a multiobjective deep reinforcement
learning with evolutionary learning algorithm for a typical complex problem
called the multiobjective vehicle routing problem with time windows (MO-VRPTW).
In the proposed algorithm, the decomposition strategy is applied to generate
subproblems for a set of attention models. The comprehensive context
information is introduced to further enhance the attention models. The
evolutionary learning is also employed to fine-tune the parameters of the
models. The experimental results on MO-VRPTW instances demonstrate the
superiority of the proposed algorithm over other learning-based and
iterative-based approaches.
