基于Matlab求解高铁运营公司列车开行优化问题

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智能优化算法  神经网络预测雷达通信 无线传感器

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⛄ 内容介绍

⛄ 完整代码

clc;

clear;

close all

Kf =[23.5, 23.5, 23, 23.5,22.5, 23, 23, 22.5, 24, 23, 21.5, 21.5, 21, 21.5, 21];

Bf =[0.75, 0.75, 0.8, 0.75, 0.85, 0.8, 0.8, 0.85, 0.7, 0.8, 0.85, 0.85, 0.9, 0.85, 0.9];

c = [];

%% 定义目标函数的系数

for t = 1:9

   c=[c;Kf'];

end

%% 限定 求解的自变量 为正整数

intcon=1:135;

lb=zeros(135,1); % 自变量的下限， >= 0

%% 定义约束条件的系数矩阵

a1 = [ones(1,15),zeros(1,15*8)];

a2 = [zeros(1,15),ones(1,15),zeros(1,15*7)];

a3 = [zeros(1,15*2),ones(1,15),zeros(1,15*6)];

a4 = [zeros(1,15*3),ones(1,15),zeros(1,15*5)];

a5 = [zeros(1,15*4),ones(1,15),zeros(1,15*4)];

a6 = [zeros(1,15*5),ones(1,15),zeros(1,15*3)];

a7 = [zeros(1,15*6),ones(1,15),zeros(1,15*2)];

a8 = [zeros(1,15*7),ones(1,15),zeros(1,15)];

a9 = [zeros(1,15*8),ones(1,15)];

D = 720;

b1 = [-D.*Bf,zeros(1,15*8)];

b2 = [zeros(1,15),-D.*Bf,zeros(1,15*7)];

b3 = [zeros(1,15*2),-D.*Bf,zeros(1,15*6)];

b4 = [zeros(1,15*3),-D.*Bf,zeros(1,15*5)];

b5 = [zeros(1,15*4),-D.*Bf,zeros(1,15*4)];

b6 = [zeros(1,15*5),-D.*Bf,zeros(1,15*3)];

b7 = [zeros(1,15*6),-D.*Bf,zeros(1,15*2)];

b8 = [zeros(1,15*7),-D.*Bf,zeros(1,15)];

b9 = [zeros(1,15*8),-D.*Bf];

Z = [1,0,1,1,1;0,1,1,1,1;1,0,0,1,1;1,1,0,1,1;0,0,0,1,1;0,0,1,1,1;0,1,0,1,1;1,0,0,0,1;1,1,1,1,1;1,1,0,0,1;1,1,0,1,0;0,1,1,1,0;0,1,0,1,0;1,1,1,1,0;1,0,0,1,0];

g2 =repmat([-D*Z(:,1)'.*Bf],1, 9);

g3 =repmat([-D*Z(:,2)'.*Bf],1, 9);

g4 =repmat([-D*Z(:,3)'.*Bf],1, 9);

g5 =repmat([-D*Z(:,4)'.*Bf],1, 9);

g6 =repmat([-D*Z(:,5)'.*Bf],1, 9);

a=[a1;a2;a3;a4;a5;a6;a7;a8;a9;b1;b2;b3;b4;b5;b6;b7;b8;b9;g2;g3;g4;g5;g6];

%% 定义约束条件 不等式右边的值

St = 6*ones(9,1);

yt = [1710;2250;2700;2790;2745;2700;2745;2790;1170];

yg = [4320;2159;2159;6481;6481];

b = [St;-yt;-yg];

%% 执行intlinprog，求解约束最优化问题

[x,fval]=intlinprog(c,intcon,a,b,[],[],lb);

xx = reshape(x,15,9);

disp(xx);

%% 堆叠柱状图

X = 1:9;

Y = xx';

a=bar(X,Y,0.4,'stacked');%绘制

%% 设置堆叠柱状图的颜色

set(a(1),'facecolor',[1, 0, 0]);

set(a(2),'facecolor',[0, 1, 0]);

set(a(3),'facecolor',[0, 0, 1]);

set(a(4),'facecolor',[0.5, 0.5, 0.5]);

set(a(5),'facecolor',[0.98, 0.75, 0.00]);

set(a(6),'facecolor',[0.85, 0.84, 0.5]);

set(a(7),'facecolor',[0.26, 0.45, 0.77]);

set(a(8),'facecolor',[0, 0.33, 0.64]);

set(a(9),'facecolor',[0.89, 0, 0.57]);

set(a(10),'facecolor',[0.9, 0.75, 0.00]);

set(a(11),'facecolor',[0.85, 0.84, 0.86]);

set(a(12),'facecolor',[0.26, 0.45, 0.77]);

set(a(13),'facecolor',[0.21, 0.33, 0.64]);

set(a(14),'facecolor',[0.89, 0.88, 0.57]);

set(a(15),'facecolor',[0.98, 0.75, 0.00]);

legend(a,'f1','f2','f3','f4','f5','f6','f7','f8','f9','f10','f11','f12','f13','f14','f15','location','southoutside','orientation','horizontal');

xlabel('时段'),

ylabel('不同类型列车开行数量/列')

⛄ 运行结果

⛄ 参考文献

❤️部分理论引用网络文献，若有侵权联系博主删除

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