| 基于优势排序数的多目标多式联运方案优化 |
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| 引用本文： | 赖志柱,戈冬梅,张云艳.基于优势排序数的多目标多式联运方案优化[J].西南师范大学学报(自然科学版),2019,44(9):60-67. |
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| 作者姓名： | 赖志柱 戈冬梅 张云艳 |
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| 作者单位： | 华东师范大学地理信息科学教育部重点实验室;贵州工程应用技术学院理学院;贵州工程应用技术学院生态工程学院 |
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| 基金项目： | 贵州省教育厅自然科学研究基金项目（黔教科2010072）；贵州省科技厅联合基金项目（黔科合J字LKB[2012]23，黔科合J字LKB[2013]24，黔科合LH字[2014]7532号）；贵州省本科教学工程项目（黔教高发（2015）337号）；贵州省一流大学（一期）重点建设项目-一流课程重点建设项目（黔教高发（2017）158号）；毕节市社科联合基金青年项目（LHQN1715）. |
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| 摘 要： | 针对带服务时间窗的多式联运方案优化问题,考虑运输总成本和运输过程中不准点导致的延误总时间两个目标,建立了多时间窗多目标多式联运数学模型,引入基于分目标的优势排序数和总优势排序数概念,证明了优势排序数的若干重要性质,依据总优势排序数的性质构建适应度函数,设计了一种基于优势排序数及寻求Pareto最优解的多目标离散粒子群算法,案例结果表明了模型和算法的可行性和有效性,算法给出的Pareto最优解也从实践角度证明了总优势排序数的性质.
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| 关 键 词： | 多式联运 粒子群算法 多目标 优势排序数 Pareto最优解 |
| 收稿时间： | 2017/11/22 0:00:00 |
Scheme Selection of Multi-objective Multimodal Transportation based on Dominance Ranking Number |
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| LAI Zhi-zhu,GE Dong-mei,ZHANG Yun-yan.Scheme Selection of Multi-objective Multimodal Transportation based on Dominance Ranking Number[J].Journal of Southwest China Normal University(Natural Science),2019,44(9):60-67. |
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| Authors: | LAI Zhi-zhu GE Dong-mei ZHANG Yun-yan |
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| Institution: | 1. Key Laboratory of Geographic Information Science, Ministry of Education of China, East China Normal University, Shanghai 200241, China;2. School of Science, Guizhou University of Engineering Science, Bijie Guizhou, 551700, China;3. School of Ecological Engineering, Guizhou University of Engineering Science, Bijie Guizhou, 551700, China |
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| Abstract: | To solve the selection of Multimodal Transportation scheme with service time windows of nodes, a multi-windows multi-objective mathematical model has been put forward, considering total transportation costs and the total delay time of transportation. The concepts of Dominance Ranking Number based on partial goal and Total Dominance Ranking Number (TDRN) has been introduced,and then several conclusions been proved to establish the fitness function. So A Multi-objective Discrete Particle Swarm Optimization Algorithm (MDPSO) based on TDRN and Pareto techniques has been developed. At last, the numerical examples have been used to demonstrate the effectiveness of the model and the algorithm. |
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| Keywords: | multimodal transportation particle swarm optimization multi-objective dominance ranking number Pareto optimal |
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