What question did this study set out to answer?

The aim is to optimise logistics distribution routes while minimising carbon emissions using advanced algorithms.

March 27, 2026Open Access

Green logistics distribution route algorithm based on carbon emissions optimisation

Key Points

The aim is to optimise logistics distribution routes while minimising carbon emissions using advanced algorithms.
Developed an optimisation model for logistics distribution networks
Introduced an adaptive genetic algorithm to adjust crossover and mutation rates
Constructed a carbon emission optimisation model considering fuel consumption and traffic constraints
Compared the performance of AGA with genetic algorithms, ant colony algorithms, and particle swarm algorithms
AGA showed improved performance in reducing carbon emissions across all scenarios
Significantly shortened delivery routes compared to other algorithms
Enhanced global search capability and convergence speed of the optimisation process

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.

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