Logistics distribution center site selection method based on improved dung beetle optimization algorithm

By improving the dung beetle optimization algorithm, combining chaos mapping and reverse learning strategy to initialize the population, and simulating dung beetle behavior to optimize the location, the problems of high transportation cost and low efficiency in the site selection of logistics distribution centers were solved, and efficient multi-objective optimization and fast convergence were achieved.

CN120672384AInactive Publication Date: 2025-09-19南陵县邮政业发展中心
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Patent Information

Application Number
CN202510844130.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-23
Publication Date
2025-09-19
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing logistics distribution center site selection method lacks comprehensiveness when considering transportation distance, resulting in high logistics costs, long delivery time, and low efficiency. In addition, the swarm intelligence optimization algorithm is prone to fall into local optimality and is difficult to achieve multi-objective optimization.

Method used

An improved dung beetle optimization algorithm is used, combined with logistic chaos mapping and reverse learning strategy to initialize the population. The dung beetle position is optimized through an improved sinusoidal algorithm and adaptive weight coefficients. The five behaviors of dung beetles are simulated for search, enhancing global and local search capabilities.

Benefits of technology

It improves the accuracy and efficiency of logistics distribution center site selection, reduces the transportation and construction costs of logistics companies, shortens delivery time, and achieves multi-objective optimization and rapid convergence.

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Abstract

The invention discloses a logistics distribution center site selection method based on an improved dung beetle optimization algorithm. The method comprises the steps of obtaining initial information of a logistics distribution center and demand points; determining a target function based on the obtained initial information, and constructing a logistics distribution center site selection scheme; carrying out coding processing on the logistics distribution center site selection scheme; and solving the objective function by using an improved dung beetle optimization algorithm, including initializing a population by using chaotic mapping and reverse learning, introducing an improved sine algorithm and an adaptive weight coefficient in rolling and stealing behaviors, and obtaining an objective function value and a logistics distribution center site selection scheme output after algorithm iteration is completed. According to the method, the problems that the convergence speed is low and local optimum is likely to happen when a swarm intelligence optimization algorithm is used for site selection planning can be well solved, and meanwhile multi-target optimization is conducted on multiple factors such as transportation cost and construction cost.
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