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交通流变化下的多配送中心-多需求点配送网络优化模型

杨忠振 穆雪 朱晓聪

杨忠振, 穆雪, 朱晓聪. 交通流变化下的多配送中心-多需求点配送网络优化模型[J]. 交通运输工程学报, 2015, 15(1): 100-107. doi: 10.19818/j.cnki.1671-1637.2015.01.013
引用本文: 杨忠振, 穆雪, 朱晓聪. 交通流变化下的多配送中心-多需求点配送网络优化模型[J]. 交通运输工程学报, 2015, 15(1): 100-107. doi: 10.19818/j.cnki.1671-1637.2015.01.013
YANG Zhong-zhen, MU Xue, ZHU Xiao-cong. Optimization model of distribution network with multiple distribution centers and multiple demand points considering traffic flow change[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 100-107. doi: 10.19818/j.cnki.1671-1637.2015.01.013
Citation: YANG Zhong-zhen, MU Xue, ZHU Xiao-cong. Optimization model of distribution network with multiple distribution centers and multiple demand points considering traffic flow change[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 100-107. doi: 10.19818/j.cnki.1671-1637.2015.01.013

交通流变化下的多配送中心-多需求点配送网络优化模型

doi: 10.19818/j.cnki.1671-1637.2015.01.013
基金项目: 

国家自然科学基金项目 51078049

国家自然科学基金项目 71431001

详细信息
    作者简介:

    杨忠振(1964-), 男, 辽宁凌海人, 大连海事大学教授, 工学博士, 从事道路交通运输规划研究

  • 中图分类号: U491.51

Optimization model of distribution network with multiple distribution centers and multiple demand points considering traffic flow change

More Information
  • 摘要: 基于城市道路网结构与交通流特征, 以总配送耗时最小为目标函数, 以交通流为约束条件, 构建了双层配送网络优化模型。上层模型计算配送车辆的配送路径, 下层模型为用户均衡交通分配模型, 通过上层模型的计算结果改变下层模型中的OD出行数据, 通过下层模型的计算结果改变上层模型中的路段通行时间。利用混合式分组法、遗传算法与Frank-Wolf算法求解模型, 并以大连市某带有31个交通小区、27个需求点和4个配送中心的交通网络为例进行实例验证。计算结果表明: 当利用最短距离法求得配送方案时, 27个需求点的总配送距离为94.8km, 总配送耗时为425.2min, 计算时间为13s;考虑交通流变化后, 利用提出的双层优化模型, 27个需求点的总配送距离为109.7km, 总配送耗时为329.1min, 计算时间为256s。利用提出的双层优化模型, 虽然总配送距离增加14.9km, 但总配送耗时却缩短96.1min, 并可以一次性达到配送车辆和其他车辆相互平衡的过程, 计算速度和效率并不是最重要的因素, 可以得到更符合实际的计算结果。

     

  • 图  1  基于最短距离法的S1N1的配送路径

    Figure  1.  Distribution routing from S1 to N1 based on minimum distance method

    图  2  考虑交通流变化后的S1N1的配送路径

    Figure  2.  Distribution routing from S1 to N1 after considering traffic flow change

    图  3  算法流程

    Figure  3.  Algorithm flow

    图  4  配送中心、需求点与交通小区划分情况

    Figure  4.  Division condition of distribution centers, demand points and traffic zones

    图  5  道路网状况

    Figure  5.  Road network condition

    图  6  优化后的配送路径

    Figure  6.  Distribution routing after optimization

    图  7  基于最短距离法配送中心1向需求点21的配送路径

    Figure  7.  Distribution routing from deistribution center 1 to demand point 21 based on minimum distance method

    图  8  考虑交通流变化后配送中心1向需求点21的配送路径

    Figure  8.  Distribution routing from deistribution center 1 to demand point 21 after considering traffic flow change

    图  9  最短距离法对应的交通流状态

    Figure  9.  Traffic flow state for minimum distance method

    图  10  考虑道路交通流变化后的交通流状态

    Figure  10.  Traffic flow state after considering traffic flow change

    表  1  需求点与需求量

    Table  1.   Demand points and demand amounts

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出版历程
  • 收稿日期:  2014-09-29
  • 刊出日期:  2015-02-25

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