Robust Optimization Applied to the Vehicle Allocation Problem

The use of mathematical models and algorithms to solve the vehicle allocation problem (VAP) has proven to be a crucial tool for decisions about fleet management in freight transport. In this type of problem, uncertainties regarding the parameters are inevitable. To circumvent this issue, one can employ the robust optimization (OR) technique, which helps in modeling and problem solving by considering the uncertainty of the parameters, increasing the chances of finding a viable solution in practice. This work proposes a robust optimization model for the VAP considering uncertainties in demand, based on models for the deterministic variant of the VAP. The proposed model is validated through computational experiments using realistic examples of road freight transport in Brazil. The results of the Monte Carlo simulations demonstrate the benefits of incorporating uncertainty into the PAV to support decision-making. Acknowledgments: FAPESP