Abstract
With the rapid development of economy, the logistics industry is growing and the distribution network is becoming more and more complex. The traditional random transportation mode is prone to reverse flow and detour transportation, which leads to low transportation efficiency. In order to solve this problem, this paper proposes a logistics shortest path search algorithm based on simulated annealing. Through the simulation of the experiment, the optimal route can be found in a short time. As a result, the driving distance can be reduced and the distribution speed can be accelerated. Most importantly, the distribution efficiency of SF logistics in Fuzhou can be improved.
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Acknowledgment
This work was supported by projects of the Natural Science Foundation of Fujian Province (No. 2015J01652).
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Xu, W. et al. (2020). Shortest Path Searching for Logistics Based on Simulated Annealing Algorithm. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_1
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DOI: https://doi.org/10.1007/978-981-15-3308-2_1
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