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...

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Veröffentlicht in:Applied soft computing Jg. 175; S. 113109
Hauptverfasser: Xu, Rui, Song, Bowen, Xiao, Wei, Fan, Xing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.05.2025
Schlagworte:
Electric vehicle routing problem
Greedy randomised adaptive search procedure
Green logistics
Heterogeneous supplemental infrastructures
Scheduling optimisation
Heterogeneous supplemental infrastructures
Greedy randomised adaptive search procedure
Scheduling optimisation
Green logistics
Electric vehicle routing problem
ISSN:1568-4946
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Zusammenfassung: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. •EVRP with Heterogeneous Supplemental Infrastructure (EVRP-HSI) is addressed.•Complex charging and battery swapping strategies are integrated into the study.•An MIP model and a biased-random GRASP algorithm for the problem are proposed.•Advantages of HSI are demonstrated through extensive computational experiments.
ISSN:1568-4946
DOI:10.1016/j.asoc.2025.113109