A biased randomised GRASP for the electric vehicle routing problem with heterogeneous supplemental infrastructures

Green logistics policies have positioned electric vehicles (EVs) as the preferred choice for logistics. Prompted by technological advancements, more companies are now adopting electric logistics vehicles equipped with both charging and battery swapping capabilities. This study addresses the electric vehicle routing problem (EVRP) by integrating various charging technologies, partial charging strategies, and different battery swapping specifications. A mixed-integer programming (MIP) model is developed to minimise total logistics costs, including vehicle operating costs, energy replenishment costs, and variable mileage costs. To solve this problem, we design a biased randomised-greedy randomised adaptive search procedure (BR-GRASP) algorithm incorporating geometric distribution. This algorithm is complemented by local search operators and energy management strategies designed for heterogeneous supplemental infrastructures (HSI). For efficient iterative optimisation, we employ a variable neighbourhood descent (VND) mechanism. Computational experiments validate the effectiveness of HSI and the proposed algorithm from multiple perspectives. Additionally, a real-world case study demonstrates the significant benefits of applying our methods to a logistics company. The research findings offer decision-making recommendations and managerial insights for logistics companies adopting EVs, as well as for relevant government agencies.