  The Vehicle Routing Problem (VRP) has been widely studied throughout its
history as a way of optimizing routes by minimizing distances, and the issue of
risk in VRP has been received less attention, which is essential to increase
transport safety, to reduce accident costs and to improve delivery reliability.
In this way, this paper aims to support decision makers to plan routes for a
road freight company considering both, i.e. logistics cost and safety. An
analytical approach based on statistics was developed in which official
government data of accidents occurrences and data from cargo insurance
companies were used to estimate the risk cost of routes using the Monte Carlo
simulation. The Capacitated Vehicle Routing Problem (CVRP) was employed and
logistics and risk costs were minimized by varying a specific safety level
coefficient and the model generated solutions with safer routes, reducing risk
cost by up to approximately 18%. The accidents probabilities and risk costs of
each route represented values as expected and the main contributions of this
paper is the applicability of the approach to support decision markers to
choose routes considering safety and logistics costs, and to be a simple and
adaptable methodology for any VRP model. In addition Knime Analytics Platform
was also used to estimate the accidents probabilities and to simplify data
exploration, analysis, visualization and interpretation.

Optimization and Control;Applications