Title: Optimisation cost and carbon emission in vehicle-drone collaborative delivery under dynamic traffic conditions

Authors: Kai Wu; Zhijiang Lu; E. Bai

Addresses: School of Management, Harbin University of Commerce, Harbin, 150006, China ' School of Management, Harbin University of Commerce, Harbin, 150006, China ' School of Finance, Southwestern University of Finance and Economics, Chengdu, 611130, China

Abstract: The rising number of vehicles in urban areas has caused severe congestion in logistics, increasing delivery and carbon costs. To address this, the vehicle and drone co-delivery model (VDCDM) has become a research focus, yet existing studies lack a strong connection to urban traffic conditions. This paper develops a multi-objective path optimisation model for vehicle-drone collaborative delivery, incorporating traffic congestion to minimise carbon emissions and total delivery costs. We introduce an improved BBO algorithm (IBBO) that enhances global search capability while reducing complexity. Testing reveals stable optimisation across various traffic scenarios. Our findings on the collaborative delivery index (CDI) show that lower CDIs lead to more drone-served customers, increasing overall costs but decreasing emissions. This highlights the need for companies to assess their strategies and choose suitable CDIs, offering valuable insights for urban logistics and emergency transport applications.

Keywords: traffic congestion; VDCDM; vehicle and drone co-delivery model; low carbon; BBO.

DOI: 10.1504/IJVSMT.2025.150170

International Journal of Vehicle Systems Modelling and Testing, 2025 Vol.19 No.4, pp.374 - 402

Received: 28 Aug 2024
Accepted: 04 Dec 2024
Published online: 02 Dec 2025 *

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