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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.

Short Bio: Weiquan Wang is a postdoctoral researcher at HEC Montréal. He earned his PhD in Economics, specializing in Industrial Economics, from the University of International Business and Economics (UIBE). He specializes in developing efficient heuristic algorithms for large-scale routing problem, particularly in the context of electric vehicle transportation. He has published relevant work in journals such as European Journal of Operational Research (EJOR) and Transportation Research Part C (TRC).