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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (2): 307-317    DOI: 10.3785/j.issn.1008-973X.2021.02.011
    
Optimization of cold chain fruit path considering customer satisfaction
Lin-lin JI(),Qing-wei WANG,Hao ZHOU,Mei-mei ZHENG*()
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract  

A cold chain fruit transportation model with two objectives, i.e., cost and customer satisfaction, was proposed considering the rapid expansion of demands for cold chain fruits and increased importance of customer satisfaction. An improved satisfaction model was proposed to accurately describe the level of customer satisfaction and improve the service response capability of cold chain fruit transportation. The gray-whitening weight function was introduced to construct different levels of customer satisfaction. The customer satisfaction scores were set to divide the factors that affect the perception of satisfaction into different ranks. The survey data was used to support the perception of customer satisfaction. Meanwhile, the improved genetic algorithm (IGA) was proposed to solve the cold chain fruit transportation model. The IGA was developed by introducing the Metropolis of simulated annealing to "super individuals" and regularly updating chromosome group with three kinds of neighborhood search randomly, to avoid the rapid convergence of the genetic algorithm (GA) and reduce the destruction of high-quality populations. Comparative analysis in the case study shows that the IGA is superior to GA and genetic simulated annealing algorithm (GA-SA). And the advantage of IGA becomes more significant as the number of customers increases.

Key words： cold chain logistic optimization      genetic algorithm      simulated annealing      customer satisfaction      gray-whitening weight function     
Received: 21 July 2020      Published: 09 March 2021
CLC:  U 9  
Fund:  国家自然科学基金资助项目（71802130）；上海浦江资助项目（18PJC083）
Corresponding Authors: Mei-mei ZHENG     E-mail: 860616956@qq.com;miqi@sjtu.edu.com
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Lin-lin JI
Qing-wei WANG
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Mei-mei ZHENG
Cite this article:

Lin-lin JI,Qing-wei WANG,Hao ZHOU,Mei-mei ZHENG. Optimization of cold chain fruit path considering customer satisfaction. Journal of ZheJiang University (Engineering Science), 2021, 55(2): 307-317.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.02.011     OR     http://www.zjujournals.com/eng/Y2021/V55/I2/307


考虑顾客满意度的冷链水果路径优化

针对冷链水果需求的迅速扩大及顾客满意度重要性的不断提升，提出以成本与满意度为双目标的冷链水果运输模型. 为了准确描述顾客满意度水平，提高冷链水果运输服务的响应能力，提出改进的满意度模型；引入灰度白化权函数构造顾客满意度不同等级阶段，设置不同等级分数将影响满意度感知的因素划分成不同等级，利用调研数据支撑顾客真实满意度感知. 提出改进的遗传算法（IGA）求解该冷链水果运输模型. 此遗传算法通过对“超级个体”引入模拟退火的Metropolis准则，随机选择3种邻域搜索之一定期更新染色体群，来避免传统遗传算法的快速收敛问题以及减轻优质种群被破坏程度. 基于实例的对比分析表明，改进遗传算法的求解效果优于传统遗传（GA）、遗传模拟退火算法（GA-SA），且随着顾客人数增加，改进遗传算法优势更明显.


关键词： 冷链物流优化,  遗传算法,  模拟退火,  顾客满意度,  灰度白化权函数 
参数 参数说明
$[{S_{1i}},t_i^1,t_i^2,{S_{2i}}]$ $[t_i^1,t_i^2]$为零售商的最佳收货时间， $[{S_{1i}},{S_{2i}}]$
为零售商可容忍最大收货时间
${t_i}$ 在零售商 $i$处服务时间/h
${t_{0k}}$ 车辆 $k$从配送中心出发的时间点/h
${d_{ij}}$ 零售商 $i$与零售商 $j$的距离/km
${v_{ij}}$ 车辆经过零售商 $i$到零售商 $j$时车速/( ${\rm{km}} \cdot {{\rm{h}}^{ - 1}}$)
${q_i}$ 零售商 $i$需求水果重量/kg
$U$ 空车时单位距离燃料消耗量/( ${\rm{L}} \cdot {\rm{k}}{{\rm{m}}^{ - 1}}$)
${l_1}$ 货物卸载率/( ${\rm{kg}} \cdot \min{^{ - 1}}$)
$M$ 车辆载重限制/kg
$V$ 车辆货箱内尺寸/m3
${M_1}$ 单次行车距离限制/km
${c_1}$ 燃油价格/(元 $ \cdot {{\rm{L}}^{ - 1}}$)
${c_2}$ 制冷剂价格/(元 $ \cdot {\rm{kg}}{^{ - 1}}$)
$F$ 车辆固定费用/元
$\Delta \theta$ 水果所需温度与室外温度差值/°C
${R_1}$ 泡沫保温箱热传导系数
$Q$ 单位面积单位时间下温差为1 °C时所消耗
制冷剂的量/(kg·m?2·h?1·°C?1)
$\alpha ,\theta $ 食物耗损系数
${u_{iJ}}$ 零售商 $i$需要第J种泡沫保温箱数量， $J = 1,\cdots m$
${V_J}$ 第J种泡沫保温箱外部尺寸/ ${{\rm{m}}^3}$， $J = 1,\cdots m$
${s_J}$ 第J种泡沫保温箱内表面积/ ${{\rm{m}}^2}$， $J = 1,\cdots m$
${q^{{V_J}}}$ 第J种泡沫保温箱装载水果重量/kg， $J = 1,\cdots m$
$\rho $ 基准重量下耗油百分比
${H_1}$ 基准重量/kg
${\rm{Le}}{_1},{\rm{Le}}{_2},{\rm{Le}}{_3}$ 分别为高、中、低满意度评分
${y_{ik}}$ 0-1变量，若车辆 $k$经过零售商 $i$，则 ${y_{ik}} = 1$，否则 ${y_{ik}} = 0$
${x_{ijk}}$ 0-1变量，车辆 $k$服务完零售商 $i$再服务零售商 $j$，则 ${x_{ijk}} = 1$，否则 ${x_{ijk}} = 0$
${t_{ik}}$ 车辆 $k$到达零售商 $i$花费时间/h
${H_{ijk}}$ 车辆 $k$经过零售商 $i$、 $j$过程中车上货物载重/kg
Tab.1 Parameter values of cold chain transportation model
Fig.1 Logic diagram for satisfaction calculation
Fig.2 Improved OX crossover operator
Fig.3 Flow chart of IGA
${N'}$ ${q_i}$ $(X,Y)$ $[t_i^1,t_i^2,{S_{2i}}]$ ${N'}$ ${q_i}$ $(X,Y)$ $[t_i^1,t_i^2,{S_{2i}}]$
1 220 （11.02, 30.36） [8.0,9.83,10.5] 19 145 （27.09, 2.25） [8.0,9.50,10.5]
2 176 (5.53, 32.56） [8.0,8.50,10.0] 20 100 （18.46, 15.31） [8.0, 10.00, 11.0]
3 211 (9.12, 35.97） [8.0,9.50,11.0] 21 80 （18.06, 30.03） [9.0,10.00,10.5]
4 210 (14.71, 9.48） [8.0, 9.00, 10.0] 22 140 （30.79, 25.85） [8.5,11.00,12.0]
5 180 (7.94, 3.75） [8.0,9.33,10.0] 23 150 （28.46, 29.51） [8.0, 10.00, 11.0]
6 130 (16.00, 8.57） [8.0,9.08,10.0] 24 210 （18.56, 25.83） [8.5,9.50,11.0]
7 90 （35.09, 33.03） [8.0,9.67,11.0] 25 200 （17.76, 23.65） [8.5,10.50,12.0]
8 236 （30.16, 25.07） [8.5,9.50,10.5] 26 180 （12.93, 21.80） [8.0, 10.00, 11.0]
9 242 (38.27, 7.22） [8.5,9.67,10.5] 27 120 （35.25, 23.00） [8.0, 10.00, 11.0]
10 210 （17.07, 29.42） [8.5,11.00,12.0] 28 130 (4.76, 7.00） [8.5,9.00,10.0]
11 215 （22.48, 25.61） [8.0,9.50,10.5] 29 190 （14.60, 14.19） [9.0,9.50,10.0]
12 170 （26.02, 20.83） [8.0, 10.00, 11.0] 30 140 （17.04, 27.33） [8.0,10.00,10.5]
13 120 （24.98, 23.03） [8.0, 9.00, 10.0] 31 220 （25.48, 24.05） [9.0,9.33,10.0]
14 110 （32.52, 29.32） [8.0,10.00,11.5] 32 140 （20.69, 26.34） [8.5,10.33,11.0]
15 80 （27.57, 35.31） [8.0,10.33,11.0] 33 160 （10.86, 2.62） [8.5,9.50,10.5]
16 115 （15.30, 35.75） [9.0,10.17,10.5] 34 150 （24.05, 20.80） [9.0,9.50,11.0]
17 120 (6.01, 28.56) [8.5,9.50,10.5] 35 210 (24.43, 16.37) [8.5,11.33,12.0]
18 70 （30.32, 3.62） [8.0, 9.00, 10.0] ? ? ? ?
Tab.2 Basic information form of retailers
$J$ ${q^{ {V_J} } }/{\rm{kg} }$ ${V_J}/{{\rm{m}}^3}$ ${s_J}/{{\rm{m}}^2}$
1 40 0.58、0.47、0.33 1.08
2 20 0.45、0.33、0.29 0.65
3 12 0.39、0.27、0.23 0.44
4 4 0.28、0.17、0.18 0.23
Tab.3 Parameter values of foam insulation boxes
算法 车辆数 ${ {{Z} }_1}$ |Im1|% ${ {{Z} }_2}$ |Im2|%
GA 5 1270.5 7.4 287.3 12.3
SA-GA 5 1196.3 3.9 296.4 9.0
IGA 5 1142.5 ? 335.7 ?
Tab.4 Results of example solution
Fig.4 Cold chain transportation route planning for actual case
算例 IGA GA-SA GA
$Z_1^{{\rm{IGA}}}$ $Z_2^{{\rm{IGA}}}$ $Z_1^{{\rm{GA}} - {\rm{SA}}}$ $Z_2^{{\rm{GA}} - {\rm{SA}}}$ |Im1| /% |Im2| /% $Z_1^{{\rm{GA}}}$ $Z_2^{{\rm{GA}}}$ |Im1| /% |Im2| /%
V3010 910.1 275.5 943.7 222.6 3.7 23.8 1161.2 262.7 27.6 4.9
V3014 898.7 289.6 934.0 256.7 3.9 12.8 990.9 248.1 10.3 16.7
V3016 895.6 291.8 930.6 267.8 3.9 9.0 961.9 259.9 7.4 12.3
V3510 1168.9 301.6 1221.3 237.1 4.5 27.2 1542.8 277.3 32.0 8.8
V3514 1147.8 331.8 1198.4 282.7 4.4 17.4 1328.7 283.5 15.8 17.0
V3516 1142.5 335.7 1196.3 296.4 4.7 13.3 1270.5 287.3 11.2 16.8
V4010 1150.4 325.2 1215.0 242.3 5.6 34.2 1535.4 308.8 33.5 5.3
V4014 1118.1 381.5 1198.6 283.9 7.2 34.4 1366.5 318.1 22.2 19.9
V4016 1115.5 386.1 1205.2 319.8 8.0 20.7 1352.1 321.8 21.2 20.0
V5010 1584.3 457.4 1697.6 343.0 7.2 33.4 2002.2 383.6 26.4 19.2
V5014 1544.9 470.3 1659.9 408.3 7.4 15.2 1775.7 391.2 14.9 20.2
V5016 1514.5 470.4 1657.3 424.1 9.4 10.9 1731.3 398.3 14.3 18.1
Tab.5 Target convergence value of three algorithms under different parameters
Fig.5 Average improvement percentage of IGA under different number of customers
N′ GA GA-SA IGA
O1 O2 /s O1 O2 /s O1 O2 /s
30 18 3.0 3405 136 598 52
35 22 3.5 3821 166 650 67
40 25 4.1 4645 218 693 79
50 31 5.0 4987 293 742 95
Tab.6 Comparison of convergence number and time of three algorithms
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