| 引用本文: | 杨喆,邓立宝,狄原竹,李春磊.基于模糊需求和模糊运输时间的多式联运路径优化[J].控制理论与应用,2024,41(6):967~976.[点击复制] |
| YANG Zhe,DENG Li-bao,DI Yuan-zhu,LI Chun-lei.Multimodal transportation route optimization based on fuzzy demand and fuzzy transportation time[J].Control Theory and Technology,2024,41(6):967~976.[点击复制] |
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| 基于模糊需求和模糊运输时间的多式联运路径优化 |
| Multimodal transportation route optimization based on fuzzy demand and fuzzy transportation time |
| 摘要点击 815 全文点击 188 投稿时间：2022-06-17 修订日期：2022-11-11 |
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| DOI编号 DOI: 10.7641/CTA.2023.20537 |
| 2024,41(6):967-976 |
| 中文关键词 不确定优化 模糊 局部搜索 差分进化算法 蒙特卡罗采样 |
| 英文关键词 uncertainty optimization fuzzy local search differential evolutionary algorithm Monte Carlo sampling |
| 基金项目 国家自然科学基金项目(62176075), 山东省自然科学基金项目(ZR2021MF063)资助. |
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| 中文摘要 |
| 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, 并且提出了一种带邻域搜索策略的自适应差分进化算法. 结果表明, 改进算法获得的最终方案在蒙特卡罗采样的大多数场景下满足约束, 方案稳定性强,目标值最低. |
| 英文摘要 |
| The study of fuzzy multimodal transport path optimization considering uncertainty can enhance the robustness of transport solutions and improve the risk resistance of enterprises while meeting the dual requirements of economic and environmental protection of transport solutions. In this paper, a low-carbon and low-cost multimodal transportation path optimization model with fuzzy demand and fuzzy transportation time is established, and a general coding method based on priority is designed for the problem that continuous metaheuristic algorithm cannot directly solve the discrete combinatorial optimization model. On this basis, a special decoding method with heuristic factors is proposed to further improve the solution quality of the algorithm, and an adaptive differential evolutionary algorithm with neighborhood search strategy is proposed. The results show that the final scheme obtained by the improved algorithm satisfies the constraints in most scenarios of Monte Carlo sampling, and the scheme is stable with the lowest objective value. |
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