Version 1.0 (1.27 MB) by Zhe Yang
使用AFO算法以及其他GA和PSO算法求解不确定多式联运路径优化问题。同时和MATLAB自带的全局优化搜索器进行对比。The AFO algorithm and other GA and PSO algorithms are used to solve the uncertain


Updated 23 Aug 2022

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考虑不确定性的模糊多式联运路径优化研究,可以在满足运输方案经济环保双重要求的同时,增强运输 方案的鲁棒性,提高企业的抗风险能力。本文建立了模糊需求和模糊运输时间下低碳低成本多式联运路径优化模 型,针对连续型元启发式算法无法直接求解离散型组合优化模型的问题,设计了基于优先级的通用编码方式;在 此基础上,为进一步提高算法的求解质量,提出了带启发式因子的特殊解码方式。
Use the AFO algorithm as well as other GA and PSO algorithms to solve uncertain multimodal path optimization problems. Also compare with MATLAB's own global optimization finder.
Run main.m directly.
Requires matlab version 2021 and later.
The study of fuzzy multimodal transportation path optimization considering uncertainty can enhance the robustness of the transportation scheme and improve the enterprise's anti-risk ability while meeting the dual requirements of economic and environmental protection of the transportation scheme. In this paper, a low-carbon and low-cost multimodal transport path optimization model under fuzzy demand and fuzzy transport time is established, and a general coding method based on priority is designed to address the problem that continuous meta-heuristic algorithms cannot solve the discrete combinatorial optimization model directly; on the basis of which, in order to further improve the algorithm's solving quality, a special decoding method with heuristic factors is proposed.
[1] Yang Zhe, Deng Libao, Di Yuanzhu et al. Multimodal transportation path optimization based on fuzzy demand and fuzzy transportation time[J/OL]. Control Theory and Applications:1-9[2023-07-14].

Cite As

Zhe Yang (2023). Path-optimization-of-uncertain-multimodal-transport (, GitHub. Retrieved .


MATLAB Release Compatibility
Created with R2021b
Compatible with R2021b and later releases
Platform Compatibility
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.