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  • CN 31-1691/TQ

基于双模式更新五行环算法的多目标冷链配送

任静 项月 刘漫丹

任静, 项月, 刘漫丹. 基于双模式更新五行环算法的多目标冷链配送[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211030001
引用本文: 任静, 项月, 刘漫丹. 基于双模式更新五行环算法的多目标冷链配送[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211030001
REN Jing, XIANG Yue, LIU Mandan. Multi-Objective Cold Chain Distribution Based on Dual-Mode Updated Five-Element Cycle Algorithm[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211030001
Citation: REN Jing, XIANG Yue, LIU Mandan. Multi-Objective Cold Chain Distribution Based on Dual-Mode Updated Five-Element Cycle Algorithm[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211030001

基于双模式更新五行环算法的多目标冷链配送

doi: 10.14135/j.cnki.1006-3080.20211030001
详细信息
    作者简介:

    任静:作者简介:任 静(1995—),女,河北人,硕士生,主要研究方向为智能优化算法与路径优化。E-mail:13955409207@139.com

    通讯作者:

    刘漫丹, E-mail:liumandan@ecust.edu.cn

  • 中图分类号: TP273

Multi-Objective Cold Chain Distribution Based on Dual-Mode Updated Five-Element Cycle Algorithm

  • 摘要: 针对生产运输中广泛存在的冷链配送问题,建立了以配送成本最小化和顾客满意度最大化为目标函数的多目标冷链物流优化模型。基于五行环优化 (Five-Elements Cycle Optimization, FECO) 算法,提出了双模式更新个体的五行环优化算法(Five-Elements Cycle Optimization Algorithm of Dual-Mode Updating Individuals, FECO-DMUI),并对多目标冷链物流模型进行求解。将FECO-DMUI算法与FECO算法、NSGA-II算法、鲸鱼优化算法和灰狼优化算法进行比较,结果验证了模型与算法的有效性,同时验证了FECO-DMUI算法在多目标冷链配送问题中能更加高效地获得路径优化的最优解集。

     

  • 图  1  顾客满意度分析

    Figure  1.  Customer satisfaction analysis

    图  2  五行元素相生相克关系

    Figure  2.  Five elements complement each other

    图  3  编码与解码操作

    Figure  3.  Encoding and decoding operations

    图  4  交叉操作

    Figure  4.  Cross operation

    图  5  变异操作

    Figure  5.  Mutation operation

    图  6  个体的修复

    Figure  6.  Individual repair

    图  7  FECO-DMUI算法流程图

    Figure  7.  Algorithm flowchart of FECO-DMUI

    图  8  客户点分布图

    Figure  8.  Customer point distribution map

    图  9  路径规划图

    Figure  9.  Path planning diagrams

    图  10  最优解集分布图

    Figure  10.  Optimal solution set distribution graph

    表  1  客户点信息

    Table  1.   Customer point information

    NumberCoordinate/kmDemand/kgTime window/min
    0(35,35)
    (41,49)
    (35,17)
    (55,45)
    (55,20)
    (15,30)
    (25,30)
    (20,50)
    (10,43)
    (55,60)
    (30,60)
    (20,65)
    (50,35)
    0(0,0,1000,1000)
    110(15,56,100,120)
    27(2,70,120,200)
    313(14,64,150,200)
    419(18,160,340,450)
    526(0,42,100,138)
    63(5,76,180,240)
    75(62,150,285,320)
    89(7,89,200,250)
    916(35,115,200,260)
    1016(21,110,190,250)
    1112(62,120,200,250)
    1219(50,180,250,382)
    13(50,25)23(21,140,270,350)
    14(15,10)20(0,80,160,230)
    15(30,5)8(0,48,100,150)
    16(10,20)19(8,48,150,180)
    17(5,30)2(32,100,200,350)
    18(20,40)12(6,65,156,218)
    19(15,60)17(0,40,120,200)
    20(45,65)9(11,89,200,290)
    21(45,20)11(15,75,150,208)
    22(45,10)18(7,65,200,320)
    23(55,5)29(2,38,168,238)
    24(65,35)3(18,98,172,210)
    25(65,20)6(72,190,300,382)
    下载: 导出CSV

    表  2  优化模型参数取值

    Table  2.   Optimize model parameter values

    $ Q $$ h $$ e $$ {\mu _1} $$ {\mu _2} $$ s $$ p $$ {c_1} $$ {c_2} $$ \alpha $$ \beta $
    9524.680.511.73500.510.040.03
    下载: 导出CSV

    表  3  交叉概率比较实验

    Table  3.   Crossover probability comparison experiment

    PcHV
    0.33.3872×1020
    0.43.9753×1020
    0.53.8035×1020
    0.63.2997×1020
    0.73.5151×1020
    0.83.5615×1020
    0.93.5504×1020
    下载: 导出CSV

    表  4  变异概率比较实验

    Table  4.   Mutation probability comparison experiment

    $ {p_m} $HV
    0.1$ 3.7487 \times {10^{20}} $
    0.2$ 3.9753 \times {10^{20}} $
    0.3$ 2.9106 \times {10^{20}} $
    0.4$ 2.8600 \times {10^{20}} $
    0.5$ 1.5053 \times {10^{20}} $
    下载: 导出CSV

    表  5  尺度因子比较实验

    Table  5.   Scale factor comparison experiment

    $ {p_s} $HV
    0.2$ 1.9892 \times {10^{19}} $
    0.4$ 3.2433 \times {10^{19}}$
    0.6$ 1.1541 \times {10^{20}} $
    0.8$ 1.7562 \times {10^{20}} $
    1.0$ 3.9753 \times {10^{20}} $
    1.2$ 3.8134 \times {10^{20}} $
    1.4$ 3.8636 \times {10^{20}} $
    1.6$ 3.0664 \times {10^{20}} $
    1.8$ 3.8636 \times {10^{20}} $
    下载: 导出CSV

    表  6  给定概率比较实验

    Table  6.   Comparison experiment with given probability

    $ {p_n} $HV
    0.1$ 1.9569 \times {10^{20}} $
    0.2$ 2.1576 \times {10^{20}} $
    0.3$ 2.2579 \times {10^{20}} $
    0.4$ 2.2579 \times {10^{20}} $
    0.5$ 3.7130\times {10^{20}} $
    0.6$ 2.3583 \times {10^{20}} $
    0.7$ 3.1171 \times {10^{20}} $
    0.8$ 3.8636 \times {10^{20}} $
    0.9$ 3.9753 \times {10^{20}} $
    1.0$ 2.3583 \times {10^{20}} $
    下载: 导出CSV

    表  7  5种算法的对比结果

    Table  7.   Comparison results of five algorithms

    AlgorithmHVMinimal delivery
    cost/CNY
    Maximum customer
    satisfaction
    Average minimum
    delivery cost/CNY
    Average maximum
    customer satisfaction
    FECO-DMUI3.9753x1020398610.9446410820.9262
    NSGA-II1.7402x1020361700.8467419470.8157
    FECO7.4377x1019449700.8397447200.8570
    GWO2.2985x1020413300.9343422710.8928
    WOA1.6919x1020437400.9043438670.8542
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-30
  • 录用日期:  2022-03-16
  • 网络出版日期:  2022-04-12

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