Affiliations: School of Economics and Management, Kunming University, Kunming, Yunnan, China | E-mail: [email protected]
Correspondence:
[*]
Corresponding author: School of Economics and Management, Kunming University, Kunming, Yunnan, China. E-mail: [email protected].
Abstract: In response to the problems of low efficiency, high cost, and serious environmental pollution faced by traditional logistics scheduling methods, this article introduced the Metaheuristic algorithm into intelligent logistics scheduling and environmentally sustainable development. This article took the Metaheuristic algorithm as the research object. It was based on an in-depth analysis of its core ideas and unique advantages, combined intelligent logistics scheduling with relevant theories and methods such as green environmental protection, and innovatively constructed an intelligent logistics scheduling model based on the Metaheuristic algorithm. This article experimentally compared the effects of different Metaheuristic algorithms on total driving distance, transportation time, fuel consumption, and carbon emissions. The experimental findings indicated that the ant colony optimization (ACO) algorithm in this article performed the best among them, and the performance of traditional algorithms and Metaheuristic algorithms was also tested in terms of performance. The findings indicated that the computational accuracy of the Metaheuristic algorithm reached 97%, which was better than the traditional 80%. Experimental results have shown that the Metaheuristic algorithm is an efficient and feasible method that can improve the efficiency of logistics scheduling and environmental sustainability.
