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7.1 模型的优点
（1）充分利用Excel对非常庞杂的数据进行统计处理，为模型的建立奠定了基础。 （2）运用表格和图像相结合，对于结果的分析更加清晰，使图论问题的解答更加明了。 （3）数学软件MATLAB的运用提高了结果的可行度，数据更加精确。
（4）多方位、多角度联系实际情况对于模型进行运用，层次分析法时和车辆调度时考
虑了用户、公司、社会多方的利益。 7.2 模型的缺点
（1）本题对数据依赖性比较大，只是根据题中所给数据做了一个理想化的模型，可能
与实际不相吻合。
（2）题目信息庞杂，数据可信度不是很精确，所以对现实的预测结论存在局限性。 （3）本题数据量巨大，我们利用了简化和聚合的方法，可能使结果不是实际中的最优化模型。
九、参考文献
[1] 邓爱民，王少梅，汪利君，城市物流配送系统优化研究[J]，武汉理工大学学报：
交通科学与工程版，30(3)：481-484 ，2006。
[2] 张吉军，模糊层次分析法 (FAHP)[J]，模糊系统与数学，14(2): 80-88，2000。 [3] 高新波，模糊聚类分析及其应用[M]，西安：西安电子科技大学出版，2004。 [4] 朱晓敏，张兆强，乔魏乾，基于重心法与物流量预测的物流园区选址[J]，物流技
术，30(5): 88-92，2011。
[5] 周炳生，Floyd 算法的一个通用程序及在图论中的应用[J]，杭州应用工程技术学
院学报，11(3): 1-9，1999。
[6] Lemon_keakea4，哈密尔顿图，http://www.doc88.com/p-035415324003.html，
2013.8.17。 [7] 维尼，选址分配问题——东大公开课，http://wenku.http://m.35331.cn//view/54b08ecfaa00b52acfc7c a82.html，2013.8.18。
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十、附录
10.1附表
10.1.1附表1 区域①用户信息表 客户代码 16716 16715 16714 16698 16699 16701 16705 16702 16706 16707 2048 16713 16710 16711 2061 16708 16417 16709 16712 16703 16700 16729 16415 16721 16704 16718 16719 16717 16728 16418 16720 16726 16727 16725 16724 16722 16723 16419 16697
经度 纬度 时间 订货量 周期 方位角 107.8852896 26.41122093 2 60 1 65.86998807 107.8847011 26.41140242 2 50 1 65.70597148 与中心距离 107.8841532 107.8836235 107.8828464 107.881957 107.8814962 107.8827721 107.8796138 107.880608 107.8467636 107.8791273 107.8787281 107.8777107 107.8466446 107.8762377 107.8779826 107.8764929 107.8727602 107.8693195 107.8663141 107.861214 107.8897936 107.8882977 107.888686 107.8900801 107.888789 107.8893801 107.8594007 107.858632 107.8923879 107.8551712 107.8531945 107.8894949 107.8923411 107.8928638 107.8930046 107.8932127 107.8400045 26.41125461 26.41109336 26.41091455 26.41073521 26.41065573 26.40986558 26.41126137 26.41053239 26.42939826 26.41004045 26.41002994 26.40980056 26.42715959 26.40956074 26.40829994 26.40771963 26.40880181 26.40808677 26.40796439 26.40691079 26.39233236 26.39242431 26.3915465 26.39065911 26.39078228 26.38838178 26.40094444 26.40059595 26.38215561 26.39921824 26.39975934 26.38114897 26.37953663 26.37868117 26.37774508 26.37693535 26.39221201 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 26
100 100 60 70 20 30 40 50 26 80 50 20 16 15 20 30 15 30 40 50 100 20 40 40 30 20 20 40 40 50 50 30 30 70 20 15 50 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 2 2 2 1 2 2 2 2 2 2 1 1 2 1 1 2 2 2 2 2 4 65.58850952 65.47734207 65.30851683 65.11339281 65.01116687 65.40694887 64.49024408 64.81147298 54.54292438 64.51314692 64.4193173 64.20410769 54.85769306 63.88417053 64.43880804 64.15447524 63.16136231 62.45625693 61.79138496 60.787645 68.82960595 68.47326304 68.64325548 69.04611138 68.73609897 69.08563629 61.11277619 60.98968058 70.29820661 60.42142563 59.9439177 69.72835839 70.49825047 70.68334958 70.7882219 70.89762674 58.20444328 23.6942676 23.70268411 23.74270682 23.78335074 23.83731355 23.89677894 23.92634493 23.94658703 23.95467068 23.98054989 24.0699467 24.10023772 24.12039168 24.19228944 24.28038471 24.28778494 24.32932967 24.45908811 24.53529161 24.78041866 24.94840461 25.32294326 25.43724333 25.48818306 25.56312151 25.59913629 25.63799338 25.86299524 25.99963128 26.07477964 26.39529775 26.39611789 26.4531037 26.61006717 26.6708989 26.7411785 26.83411078 26.9114339 27.92436648
16694 16696 16695 16405 16692 16689 16688 16690 16693 16691 16686 16687 16408 16684 16683 16685 16651 16644 16407 16649 16629 16658 16412 16642 16630 16631 16643 16659 16648 16634 16632 16641 16638 16263 16628 16637 16409 16640 16635 16654 16639 16657 16645
107.8243602 107.8244913 107.8244565 107.8257411 107.825105 107.8237095 107.8205115 107.8198867 107.8189453 107.8173347 107.8551111 107.8721389 107.8562778 107.8570292 107.8555833 107.85625 107.8088048 107.8088906 107.8084528 107.8081944 107.8081229 107.8099167 107.8078889 107.8095278 107.8078719 107.8080061 107.8078298 107.8078313 107.8085747 107.8078696 107.8077566 107.8082579 107.8078447 107.8076 107.8074933 107.8076659 107.8075783 107.8076853 107.8075245 107.8076667 107.8074167 107.8076608 107.8076101 26.38839287 26.38663201 26.38571797 26.38184211 26.37916323 26.37655737 26.37556714 26.37448343 26.37183398 26.36752147 26.30786111 26.30136111 26.30433333 26.30385227 26.30433333 26.30355556 26.32254243 26.32246091 26.32224409 26.32180556 26.32169266 26.32016667 26.32122222 26.32025 26.32115657 26.32106631 26.32103382 26.32086111 26.32042043 26.32077627 26.32078974 26.32048717 26.32057343 26.32068 26.32073233 26.32060891 26.32063053 26.32056262 26.32063828 26.32050754 26.32063889 26.3204308 26.32033381 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 27
25 30 20 30 20 60 25 30 20 20 10 20 5 25 5 25 80 80 5 300 20 100 30 100 30 10 10 30 80 10 20 20 350 590 15 40 20 50 20 50 20 30 15 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 4 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 1 1 2 1 1 1 1 1 2 55.7481644 55.986159 56.09028395 56.78055235 56.97863615 57.031004 56.58604867 56.60159965 56.73916782 56.94421566 68.50142329 71.75834267 68.92288502 69.07771053 68.80814715 68.9677557 59.98819887 60.00832848 59.96100218 59.96028307 59.95933522 60.36293907 59.96507313 60.2968994 59.96823874 59.99633503 59.97258076 59.9878434 60.13818589 60.00099386 59.98280335 60.08462691 60.01487107 59.96877587 59.94817215 59.98487239 59.9698191 59.99182144 59.96103928 59.99380427 59.94477044 59.99959375 60.00038911 29.22647915 29.38009868 29.46640966 29.74606507 30.03369037 30.3606428 30.64695562 30.78555571 31.08868963 31.58721693 35.57164384 35.60778561 35.88949547 35.9094545 35.91728852 35.97117274 36.33949103 36.34256852 36.38771571 36.44421019 36.45903543 36.50689499 36.5172417 36.52022547 36.52449304 36.52571525 36.53862969 36.55514581 36.55635314 36.56117571 36.56615254 36.5674531 36.58205863 36.58539686 36.5862949 36.58857038 36.59135382 36.59194134 36.5936027 36.59827008 36.5995354 36.60597091 36.61810896
16650 16647 16633 16653 16660 16646 16636 16410 16652 16656 16655 16682 16731 16758 16756 16733 16732 16730 16757 16760 16748 16749 16759 16754 16751 16668 16413 16753 16761 16750 107.8075784 107.8075532 107.8074311 107.8074165 107.8073563 107.8071866 107.8066874 107.8056944 107.8058611 107.8062102 107.8055278 107.8585833 107.7791382 107.8207222 107.8204444 107.7797714 107.7781898 107.7766696 107.8259722 107.8303056 107.8299444 107.8275278 107.8306351 107.8275177 107.8262222 107.8063212 107.8061322 107.8274722 107.8266944 107.8254696 26.32026888 26.3201925 26.31952155 26.31933327 26.31873604 26.31878178 26.31857748 26.31872222 26.31725 26.31693243 26.31686111 26.28397222 26.32348072 26.29880556 26.29866667 26.32161875 26.32153733 26.32253657 26.28880556 26.28636111 26.28480556 26.28533333 26.2826219 26.28329675 26.28383333 26.29302706 26.29186956 26.27577778 26.27488889 26.27104877 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 2 2 30 20 300 40 40 30 20 30 15 15 20 20 20 10 10 15 15 10 20 20 10 20 20 25 20 20 20 15 30 10 1 1 1 1 1 1 1 1 1 2 1 2 1 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 60.00125767 60.00409315 60.04389049 60.05798572 60.10050087 60.07112452 60.01404004 59.85324743 60.00506204 60.08426945 59.98888684 70.51194372 55.60683217 63.70114662 63.6698223 55.86935014 55.65923846 55.35680365 65.19942348 66.01150968 66.06082329 65.66639546 66.30697774 65.80079254 65.57386299 62.02761689 62.08821144 66.28377328 66.22762809 66.29451034 36.62610984 36.63485298 36.70613832 36.72505327 36.78584365 36.79083841 36.8381538 36.8794696 37.01162176 37.02281434 37.06750574 37.9209225 38.00746177 38.03920999 38.06669746 38.13870767 38.24494853 38.24908609 38.78939213 38.83784143 39.01190484 39.06813366 39.20269354 39.27468223 39.27960606 39.33924021 39.46257622 40.03943793 40.16451524 40.60943481 10.2编程代码
10.2.1层次分析法matlab代码 m=[1 5 7; 1/5 1 2; 1/7 1/2 1]; [d,v]=eig(m) z1=[1 1/2; 2 1] [d,v]=eig(z1)
z2=[1 1/3 1/4 1/7; 3 1 1/2 1/4; 4 2 1 1/3;7 4 3 1]; [d,v]=eig(z2)
z3=[1 1 1/2;1 1 1/2;2 2 1]; [d,v]=eig(z3)
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10.2.2绘制用户点与配送中心与路图matlab代码 clc;close all;clear all; X=107.972554615162; Y=26.6060305362822; file='D:\\test\\1.xls';
[data text]=xlsread(file); y=data(:,3); x=data(:,2);
plot(x,y,'+k',X,Y,'pr'); for n=1:16764
plot(data(n,2),data(n,3),'ro')
str=['(' num2str(x(n)) ',' num2str(y(n)) ')']; text(x(n)+0.2,y(n)+1,str); end
for i=1:45442 Y1=DATA(:,2); X1=DATA(:,1); Y2=DATA(:,4); X2=DATA(:,3);
line([X1(i),X2(i)],[Y1(i),Y2(i)]); str= num2str(i);
text(X1(i)+0.2,Y1(i)+1,str) end
%?-pieí?
X=107.972554615162; Y=26.6060305362822; y=Customer(:,2); x=Customer(:,1);
plot(x,y,'+k',X,Y,'pr'); hold on
m=jpoint(:,1); n=jpoint(:,2); plot(m,n,'.r')
10.2.3 画分区内节点与配送中心与经过的路图 %?-pieí?
X=107.972554615162; Y=26.6060305362822; y=Customer(:,2); x=Customer(:,1);
plot(x,y,'+k',X,Y,'pr'); hold on
m=jpoint(:,1); n=jpoint(:,2);
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