Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (2): 307-317    DOI: 10.3785/j.issn.1008-973X.2021.02.011
机械工程     
考虑顾客满意度的冷链水果路径优化
季琳琳(),王清威,周豪,郑美妹*()
上海交通大学 机械与动力工程学院，上海 200240
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
 全文: PDF(1054 KB)   HTML
摘要：

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    
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
收稿日期: 2020-07-21 出版日期: 2021-03-09
CLC:  U 9  
基金资助: 国家自然科学基金资助项目（71802130）；上海浦江资助项目（18PJC083）
通讯作者: 郑美妹     E-mail: 860616956@qq.com;miqi@sjtu.edu.com
作者简介: 季琳琳（1994—），女，硕士，从事物流运输优化研究. orcid.org/0000-0002-1574-8340. E-mail： 860616956@qq.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　
季琳琳
王清威
周豪
郑美妹

引用本文:

季琳琳,王清威,周豪,郑美妹. 考虑顾客满意度的冷链水果路径优化[J]. 浙江大学学报(工学版), 2021, 55(2): 307-317.

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.

链接本文:

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

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
表 1  冷链运输模型参数表
图 1  满意度计算逻辑图
图 2  改进OX交叉算子
图 3  改进遗传算法结构流程图
${N'}$ ${q_i}$ $(X,Y)$ $[t_i^1,t_i^2,{S_{2i}}]$ ${N'}$ ${q_i}$ $(X,Y)$ $[t_i^1,t_i^2,{S_{2i}}]$
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] ? ? ? ?
表 2  零售商基本信息表
$J$ ${q^{ {V_J} } }/{\rm{kg} }$ ${V_J}/{{\rm{m}}^3}$ ${s_J}/{{\rm{m}}^2}$
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
表 3  泡沫保温箱参数
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 ?
表 4  实例求解结果
图 4  实际案例冷链运输路径规划
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
表 5  不同参数下3种算法目标收敛值
图 5  不同顾客人数下改进遗传算法平均改进百分比
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
表 6  3种算法收敛代数及时间比较
1 张东. 电子商务环境下冷链物流的顾客满意度研究[D]. 天津: 天津理工大学, 2018.
ZHANG Dong. Research on customer satisfaction of cold chain logistics in E-commerce environment [D]. Tianjin: Tianjin University of Technology, 2018.
2 杜志平, 胡永彪, 陈永立 基于客户满意度的生鲜农产品末端配送的VRP研究[J]. 供应链管理, 2020, 1 (1): 113- 128
DU Zhi-ping, HU Yong-biao, CHEN Yong-li Research on the vehicle routing problem in the distribution of fresh agricultural products based on customer satisfaction[J]. Supply Chain Management, 2020, 1 (1): 113- 128
3 张亚明, 李娜 基于精英单亲遗传算法的冷链物流VRP模型优化研究[J]. 数学的实践与认识, 2016, 46 (4): 87- 96
ZHANG Ya-ming, LI Na Research on elite selection based partheno-genetic algorithm under optimized cold chain logistics VRP model[J]. Mathematics in Practice and Theory, 2016, 46 (4): 87- 96
4 YANG B, HU ZH, WEI C, et al Routing with time-windows for multiple environmental vehicle types[J]. Computers and Industrial Engineering, 2015, 89: 150- 161
doi: 10.1016/j.cie.2015.02.001
5 RABBANI M, TAHERI M, RAVANBAKHSH M A bi-objective vehicle routing problem with time window by considering customer satisfaction[J]. International Journal of Strategic Decision Sciences, 2016, 7 (2): 16- 39
doi: 10.4018/IJSDS.2016040102
6 SIVARAMKUMAR V, THANSEKHAR M R, SARAVANAN R, et al Multi-objective vehicle routing problem with time windows: improving customer satisfaction by considering gap time[J]. Part B: Journal of Engineering Manufacture, 2017, 231 (7): 1248- 1263
doi: 10.1177/0954405415586608
7 韩亚娟, 彭运芳, 魏航, 等 超启发式遗传算法求解带软时间窗的车辆路径问题[J]. 计算机集成制造系统, 2019, 25 (10): 2571- 2579
HAN Ya-juan, PENG Yun-fang, WEI Hang, et al Hyper heuristic genetic algorithm for vehicle routing problem with soft time windows[J]. Computer Integrated Manufacturing Systems, 2019, 25 (10): 2571- 2579
8 ZHANG H Z, ZHANG Q W, MA L, et al A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows[J]. Information Sciences, 2019, 490: 166- 190
doi: 10.1016/j.ins.2019.03.070
9 ZULVIA F E, KUO R J, NUGROHO D Y A many-objective gradient evolution algorithm for solving a green vehicle routing problem with time windows and time dependency for perishable products[J]. Journal of Cleaner Production, 2020, 242: 118428
doi: 10.1016/j.jclepro.2019.118428
10 AFSHAR B M, MEHRABI A, SAFARI H, et al A green vehicle routing problem with customer satisfaction criteria[J]. Journal of Industrial Engineering International, 2016, 12 (4): 529- 544
doi: 10.1007/s40092-016-0163-9
11 梁承姬, 黄涛, 徐德洪, 等 改进遗传算法求解带模糊时间窗冷链配送问题[J]. 广西大学学报, 2016, 41 (3): 826- 835
LIANG Cheng-ji, HUANG Tao, XU De-hong, et al A solution for cold chain distribution with fuzzy time window based on improved Genetic Algorithm[J]. Journal of Guangxi University, 2016, 41 (3): 826- 835
12 王娇. 基于多目标决策的生鲜电商联合配送车辆路径优化研究[D].重庆: 重庆大学, 2017.
WANG Jiao. Study on optimization of joint distribution VRP in fresh product E-commerce based on multi-objective decision [D]. Chongqing: Chongqing University, 2017.
13 邓爱民, 毛超, 周彦霆 带软时间窗的集配货一体化 VRP 改进模拟退火算法优化研究[J]. 系统工程理论与实践, 2009, 29 (5): 186- 192
DENG Ai-min, MAO Chao, ZHOU Yan-ting Optimizing research of an improved simulated annealing algorithm to soft time windows vehicle routing problem with pich-up and delivery[J]. Systems Engineering: Theory and Practice, 2009, 29 (5): 186- 192
doi: 10.3321/j.issn:1000-6788.2009.05.024
14 林清国. 基于混合遗传算法的有时间窗车辆路径问题研究[D]. 济南: 山东大学, 2007.
LIN Qing-guo. Research of the vehicle routing problem with time windows based on hybrid Genetic Algorithm [D]. Jinan: Shandong University, 2007.
15 高志波, 龙科军, 王倩, 等 车辆路线问题的自适应遗传模拟退火算法[J]. 中国科技论文, 2017, 12 (7): 764- 769
GAO Zhi-bo, LONG Ke-jun, WANG Qian, et al A self adaptive genetically simulated annealing algorithm of vehicle routing problem[J]. China Sciencepaper, 2017, 12 (7): 764- 769
doi: 10.3969/j.issn.2095-2783.2017.07.009
16 RABBANI M, TAHAEI M, FARROKHI A H, et al. Using meta-heuristic algorithms and hybrid of them to solve multi compartment vehicle routing problem [C]// 2017 Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management. Singapore: IEEE, 2017: 1022-1026.
17 XIAO Y Y, ZHAO Q H, KAKU I, et al Development of a fuel consumption optimization model for the capacitated vehicle routing problem[J]. Computers and Operations Research, 2012, 39: 1419- 1431
doi: 10.1016/j.cor.2011.08.013
18 杨珍花, 赖平仲, 汤洋, 等 冷藏车多车型混合配送调度优化[J]. 系统工程, 2015, 33 (10): 28- 36
YANG Zhen-hua, LAN Ping-zhong, TANG Yang, et al Mix distribution dispatch optimization by multi-type refrigerated trucks[J]. Systems Engineering, 2015, 33 (10): 28- 36
19 庞敏, 张建东, 刘明阳 基于白化权函数聚类法的航电系统效能评估[J]. 计算机仿真, 2012, 29 (4): 112- 129
PANG Min, ZHANG Jian-dong, LIU Ming-yang Evaluation of avionics system based on whitenization weight function clustering method[J]. Computer Simulation, 2012, 29 (4): 112- 129
doi: 10.3969/j.issn.1006-9348.2012.04.027
20 韩晓龙. 水果物流网络及节点布局研究[D]. 武汉: 华中农业大学, 2009.
HAN Xiao-long. Research on fruit logistics network and nodes layout [D]. Wuhan: Huazhong Agricultural University, 2009.
21 EMRAH D, TOLGA B, GILBERT L The bi-objective pollution-routing problem[J]. European Journal of Operational Research, 2014, 232: 464- 478
doi: 10.1016/j.ejor.2013.08.002
22 康凯, 韩杰, 普玮, 等 生鲜农产品冷链物流低碳配送路径优化研究[J]. 计算机工程与应用, 2019, 55 (2): 259- 265
KANG Kai, HAN Jie, PU Wei, et al Optimization research on cold chain distribution routes considering carbon emissions for fresh agricultural products[J]. Computer Engineering and Applications, 2019, 55 (2): 259- 265
doi: 10.3778/j.issn.1002-8331.1709-0276
23 HERRERA F, LOZANO M, VERDEGAY J L Tackling real-coded genetic algorithms: operators and tools for behavioral analysis[J]. Artificial Intelligence Review, 1998, 12 (4): 265- 319
doi: 10.1023/A:1006504901164
24 CHEN Z, LU Z X, QIAN J A new non-geometric transmission parameter optimization design method for HMCVT based on improved GA and maximum transmission efficiency[J]. Computers and Electronics in Agriculture, 2019, 167: 105034
doi: 10.1016/j.compag.2019.105034
25 GENDREAU M, HERTZ A, LAPORTE G A tabu search heuristicfor the vehicle routing problem[J]. Management Science, 1994, 40 (10): 1276- 1290
doi: 10.1016/j.energy.2016.05.105
26 XIN J F, ZHONG J B, LI S X, et al Greedy mechanism based particle swarm optimization for path planning problem of an unmanned surface vehicle[J]. Sensors, 2019, 19 (21): 4620
doi: 10.3390/s19214620
27 BABIN G, DENEAULT S, LAPORTE G Improvements to the or-opt heuristic for the symmetric travelling salesman problem[J]. Journal of the Operational Research Society, 2007, 58 (3): 402- 407
doi: 10.1057/palgrave.jors.2602160
[1] 张铁,胡亮亮,邹焱飚. 基于混合遗传算法的机器人改进摩擦模型辨识[J]. 浙江大学学报(工学版), 2021, 55(5): 801-809.
[2] 齐平,束红. 智慧医疗场景下考虑终端移动性的任务卸载策略[J]. 浙江大学学报(工学版), 2020, 54(6): 1126-1137.
[3] 孟祥飞,王仁广,徐元利. 双行星排汽车纯电驱动模式的转矩分配策略[J]. 浙江大学学报(工学版), 2020, 54(11): 2214-2223.
[4] 粟序明,方成刚,潘裕斌,吴伟伟,李亚萍,朱浪. 变照度下的视觉测量系统误差建模[J]. 浙江大学学报(工学版), 2020, 54(10): 1929-1935.
[5] 谭芳芳,朱俊江,严天宏,高志强,何岭松. 基于GA-WPT-ELM的6061铝合金表面粗糙度预测[J]. 浙江大学学报(工学版), 2020, 54(1): 40-47.
[6] 余煇,柴登峰. 基于长方形点过程的遥感图像汽车提取[J]. 浙江大学学报(工学版), 2019, 53(9): 1741-1748.
[7] 张曼, 施树明. 面向汽车运行工况设计的马氏链非等长交叉进化算法[J]. 浙江大学学报(工学版), 2018, 52(9): 1658-1666.
[8] 焦龙龙, 罗森林, 刘望桐, 潘丽敏, 张笈. 基于遗传算法的二进制程序模糊测试方法[J]. 浙江大学学报(工学版), 2018, 52(5): 1014-1019.
[9] 黄家骏, 腾来, 张朝杰, 王春晖, 朴成勇. 基于模拟退火算法的I/Q不平衡校正[J]. 浙江大学学报(工学版), 2018, 52(11): 2218-2225.
[10] 朱卓悦, 徐志刚, 沈卫东, 杨得玉. 基于遗传蝙蝠算法的选择性拆卸序列规划[J]. 浙江大学学报(工学版), 2018, 52(11): 2120-2127.
[11] 赵斌, 张松, 李剑峰. 基于零件摩擦学性能的磨削参数优化[J]. 浙江大学学报(工学版), 2018, 52(1): 16-23.
[12] 张玄武, 郑耀, 杨波威, 张继发. 基于级联前向网络的翼型优化设计[J]. 浙江大学学报(工学版), 2017, 51(7): 1405-1411.
[13] 袁友伟, 余佳, 郑宏升, 王娇娇. 基于新颖性排名和多服务质量的云工作流调度算法[J]. 浙江大学学报(工学版), 2017, 51(6): 1190-1196.
[14] 张丽娜, 余阳. 海量O2O服务组合的优化[J]. 浙江大学学报(工学版), 2017, 51(6): 1259-1268.
[15] 苏亮, 宋明亮, 董石麟, 罗尧治. 循环遗传聚类法稳定图自动分析[J]. 浙江大学学报(工学版), 2017, 51(3): 514-523.