Orateur
Description
In this study, we examine the optimization of fleet size and mix, together with vehicle routing, under uncertain demand conditions, with explicit consideration of sustainability aspects in the context of Last Mile logistics. We propose a two-stage bi-objective stochastic mixed-integer programming model that simultaneously minimizes total costs and vehicle emissions associated with delivery activities. The first-stage tactical decisions involve determining the fleet size, composition, and consistent routing, whereas the second-stage operational decisions pertain to the allocation of parcels to be delivered and the selection of customers to be served by an external delivery provider. The ε-constraint method is applied to transform the bi-objective problem into a single-objective formulation, enabling the identification of all Pareto-optimal solutions. To cope with the computational challenges posed by real-world instances, an L-shaped algorithm is designed and implemented within the ε-constraint framework. The performance of the L-shaped approach is evaluated, demonstrating that it provides cost-effective solutions in short computational time. Managerial insights are finally discussed.
