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Title: Green logistics distribution route algorithm based on carbon emissions optimisation

Authors: Lin Zhu; Chulv Sun

Addresses: School of Logistics Engineering, Tianjin Vocational College of Transportation, Tianjin, 300000, China ' School of Economics and Management, Tianjin University of Technology and Education, Tianjin, 300000, China

Abstract: Aiming at the problems of high carbon emissions (CE) and low optimisation efficiency in green logistics distribution (LD) path optimisation, this paper takes CE as the goal and introduces an adaptive genetic algorithm (AGA) to dynamically adjust the crossover and mutation probabilities, reduce CE, and improve the global search capability and convergence speed. This paper first constructs an optimisation model based on the basic data of the LD network, and then constructs a carbon emission optimisation model based on fuel consumption and CE taking into account time windows and traffic constraints. Finally, this paper analyses the performance of genetic algorithm (GA), ant colony algorithm (ACO), particle swarm algorithm (PSO) and AGA algorithm in carbon emission reduction and path optimisation by comparing their optimisation results. The results show that the AGA algorithm performs well in all test scenarios, successfully reduces CE, and significantly shortens the delivery route.

Keywords: green logistics; distribution route; route optimisation; carbon emissions; AGA; adaptive genetic algorithm; optimise efficiency.

DOI: 10.1504/IJEP.2026.152500

International Journal of Environment and Pollution, 2026 Vol.76 No.5, pp.1 - 20

Received: 27 Feb 2025
Accepted: 04 Sep 2025
Published online: 24 Mar 2026 *