Séminaire en format hybride au GERAD local 4488 ou Zoom.

We investigate the Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions (HEVRP-NL), which involves routing a heterogeneous fleet of electric vehicles. This problem accounts for multiple charging modes and time-dependent waiting time functions at charging stations. We model the problem using a path-based mixed-integer linear programming formulation. To solve it, we present an algorithmic framework consisting of two components. The first component is an iterated local search algorithm with a problem-specific route evaluation function that generates local optimal solutions and creates a pool of high-quality routes. The second component is a set-partitioning model that combines a subset of these routes. We design benchmark instances for HEVRP-NL, which are used to evaluate our approach. For small-scale HEVRP-NL instances, the proposed model can be applied within a general-purpose mixed-integer programming solver to achieve optimal solutions or generate tight upper bounds. Extensive computational results on large-scale HEVRP-NL instances demonstrate the benefits of incorporating nonlinear charging functions. Finally, we conduct experiments on related benchmark instances, showing that our algorithm outperforms existing approaches from the literature.