A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems


  • Chunxia Zhai Department of Logistics Management School of Humanities and Management Xi’an Traffic Engineering Institute, Xi’an 710300, Shaanxi, China




green low-carbon logistics path optimization, snow-melt optimization algorithm, position-order array coding, distribution path scheme


INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery.

OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building.

METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments.

RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value.

Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved.


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How to Cite

Zhai C. A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems. EAI Endorsed Trans Energy Web [Internet]. 2024 Jan. 18 [cited 2024 Feb. 22];11. Available from: https://publications.eai.eu/index.php/ew/article/view/4889