Cold Chain Low-carbon Logistics Path Optimisation Method Based on Improved Hummingbird Optimisation Algorithm

Xuan Long

doi:10.4108/ew.4991

Authors

Xuan Long School of international trade Hainan College of Economics and Business, Hai'kou 571127, Hainan , China

DOI:

https://doi.org/10.4108/ew.4991

Keywords:

cold chain low-carbon logistics path optimisation, Hummingbird optimisation algorithm, Jiaden set improvement strategy, two-layer coding approach

Abstract

INTRODUCTION:The research of scientific and reasonable logistics and distribution programme time the pursuit of each logistics enterprise, not only can improve customer satisfaction and corporate image, but also help to reduce distribution costs.

OBJECTIVES: For the current cold chain low-carbon logistics distribution path optimisation methods there are problems such as easy to fall into the local optimum, optimisation time-consuming.

METHODS: This paper proposes a cold chain low-carbon logistics distribution path optimisation method based on the improved Hummingbird optimisation algorithm. Firstly, by analyzing the characteristics of the cold chain low-carbon logistics distribution path optimization problem, designing the cold chain low-carbon logistics path optimization objective function and constraints, and constructing a cold chain low-carbon logistics distribution path optimization model based on a soft time window; then, the hummingbird optimization algorithm is improved by using the initialization strategy of the set of good points and the cardinality leap strategy, to overcome the defects of the hummingbird optimization algorithm; secondly, a method based on intelligent optimization algorithm is proposed by designing the double-layer array coding and the adaptive function, combined with the improved hummingbird optimization algorithm. A cold chain low-carbon logistics path optimization method based on intelligent optimization algorithm is proposed; finally, the superiority and robustness of the proposed method are verified by simulation experimental analysis.

RESULTS: The results show that the proposed method not only improves the optimisation time, but also increases the optimisation fitness value.

CONCLUSION: This paper solves the problem that the optimisation of the green low-carbon logistics path optimisation problem is time-consuming and prone to falling into local optimum.

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