  The Multi-Objective Vehicle Routing Problem (MOVRP) is a complex optimization
problem in the transportation and logistics industry. This paper proposes a
novel approach to the MOVRP that aims to create routes that consider drivers'
and operators' decisions and preferences. We evaluate two approaches to address
this objective: visually attractive route planning and data mining of
historical driver behavior to plan similar routes. Using a real-world dataset
provided by Amazon, we demonstrate that data mining of historical patterns is
more effective than visual attractiveness metrics found in the literature.
Furthermore, we propose a bi-objective problem to balance the similarity of
routes to historical routes and minimize routing costs. We propose a two-stage
GRASP algorithm with heuristic box splitting to solve this problem. The
proposed algorithm aims to approximate the Pareto front and to present routes
that cover a wide range of the objective function space. The results
demonstrate that our approach can generate a small number of non-dominated
solutions per instance, which can help decision-makers to identify trade-offs
between routing costs and drivers' preferences. Our approach has the potential
to enhance the last-mile delivery operations of logistics companies by
balancing these conflicting objectives.
