Research on Green Distribution Problems of Mixed Fleets Considering Multiple Charging Methods

Lvjiang Yin (), Ruixue Zhu and Dandan Jian
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Lvjiang Yin: Automobile Business School, Hubei University of Automotive Technology, Shiyan 442002, China
Ruixue Zhu: Automobile Business School, Hubei University of Automotive Technology, Shiyan 442002, China
Dandan Jian: Automobile Business School, Hubei University of Automotive Technology, Shiyan 442002, China

Energies, 2025, vol. 18, issue 19, 1-25

Abstract: Against the backdrop of global emissions reduction and transportation electrification, electric vehicles are gradually replacing traditional fuel vehicles for delivery. However, issues such as limited range and charging times often conflict with time window service requirements. To balance economic and environmental performance, mixed fleets and multi-method charging strategies have emerged as viable approaches. This study addresses the problem by developing a mixed-integer programming model that incorporates multiple charging methods and carbon emission accounting. An Improved Adaptive Large Neighborhood Search (IALNS) algorithm is proposed, featuring multiple Removal and Insertion operators tailored for customers and charging stations, along with two local optimization operators. The algorithm’s superiority and applicability are validated through simulation and comparative analysis on benchmark instances and real-world data from an urban courier network. Sensitivity analysis further demonstrates that the proposed algorithm effectively coordinates vehicle type and charging mode selection, reducing total costs and carbon emissions while ensuring service quality. This approach provides practical reference value for operational decision-making in mixed fleet delivery.

Keywords: mixed fleet; vehicle routing problem; adaptive large neighborhood search; carbon emissions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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