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????个人信条：格物致知。?
? 内容介绍
随着”一带一路”国家战略的推进和建设,中新物流通道在衔接”丝绸之路经济带”和”海上丝绸之路”中的作用日益明显.集装箱多式联运是中新贸易的主要实现形式,目前,多式联运路径优化问题是多式联运相关研究领域的热点.然而多式联运路径优化过程即不同参与者的权衡过程,由于不同商品的特性差异,运输活动中不同参与者对集装箱多式联运的要求也不尽相同.随着”绿色物流”逐渐深入人们的生活,运输过程中的碳排放也受到政府部门越来越多的关注.
? 部分代码
clc;
clear all;
close all;
warning off
%%
noRng=1;
rng(‘default’)
rng(noRng)
%%
global data
data.numN=15; %节点数量
data.Cap_Ts=xlsread(“节点处的最大中转运输能力.xlsx”);
temp=round(rand(data.numN,3)*10+15)*10;
data.Cap_Ts(:,2:end)=temp;
data.Windows=xlsread(“节点的时间窗.xlsx”);
data.D=xlsread(“节点间距离.xlsx”);
data.Cap_Tp=xlsread(“节点间最大运输能力.xlsx”);
data.T=data.D;
data.v=[76,60,30];
for i=1:length(data.Cap_Tp(:,1))
no1=data.Cap_Tp(i,1);
no2=data.Cap_Tp(i,2);
for j=1:3
if isnan(data.Cap_Tp(i,2+j))
data.D(i,2+j)=nan;
end
end
data.T(i,[3,6,9])=round(data.D(i,3:5)./data.v/1.2);
data.T(i,[4,7,10])=round(data.D(i,3:5)./data.v);
data.T(i,[5,8,11])=round(data.D(i,3:5)./data.v/0.8);
end
data.Windows(:,3)=data.Windows(:,3).*(1-rand(size(data.Windows(:,3))));
data.Windows(:,4)=data.Windows(:,4).*(1+rand(size(data.Windows(:,3))))+10;
data.Windows(:,3:4)=max(0,round(data.Windows(:,3:4)+randn(size(data.Windows(:,3:4)))));
data.Windows(:,5)=max(data.Windows(:,4));
data.CT=[0,3.09,5.23; % 转换成本
3.09,0,26.62;
5.23,26.62,0];
data.TT=[0,1,1; %转换时间
1,0,2;
1,2,0];
data.ET=[0,1.56,6; % 转换碳排放
1.56,0,3.12;
6,3.12,0];
data.q=[120,150,180];
data.E0=[0.796,0.028,0.04];
data.CW=[30,50];
data.S=1;
data.E=15;
data.alpha=0.8;
data.beta1=0.8;
data.beta2=0.8;
data.beta3=0.8;
data.C0=[0.3,0.2,0.1]; %三种运输方式的运输成本
data.weight=[1,1];
data.maxB=100000;
data.maxE=21000;
%%
%%
data.numQ=100;
for i=1:data.numQ
if rand<0.5
data.q0(i)=rand*(data.q(2)-data.q(1))+data.q(1);
else
data.q0(i)=rand*(data.q(3)-data.q(2))+data.q(2);
end
end
%%
G=graph(data.D(:,1),data.D(:,2),data.D(:,1)*0+1);
figure
plot(G)
set(gca,’LooseInset’,get(gca,’TightInset’))
%%
lb=0;
ub=1;
dim=length(data.D(:,1))*3;
option.lb=lb;
option.ub=ub;
option.dim=dim;
if length(option.lb)==1
option.lb=ones(1,option.dim)*option.lb;
option.ub=ones(1,option.dim)*option.ub;
end
option.fobj=@aimFcn_1;
option.showIter=0;
%% 算法参数设置 Parameters
% 基本参数
option.numAgent=20; %种群个体数 size of population
option.maxIteration=100; %最大迭代次数 maximum number of interation
%% 遗传算法
option.p1_GA=0.7;
option.p2_GA=0.1;
%% 粒子群
option.w_pso=0.1;
option.c1_pso=1.2;
option.c2_pso=1.2;
%% AFO
option.v_lb=-(option.ub-option.lb)/4;
option.v_ub=(option.ub-option.lb)/4;
option.w2=0.5; %weight of Moving strategy III
option.w4=1;%weight of Moving strategy III
option.w5=1;%weight of Moving strategy III
option.pe=0.01; % rate to judge Premature convergence
option.gap0=ceil(sqrt(option.maxIteration*2))+1;
option.gapMin=5; % min gap
option.dec=2; % dec of gap
option.L=10; % Catastrophe
%% DE
option.F=0.5;
option.CR=0.5;
%%
str_legend=[{‘GA’},{‘PSO’},{‘AFO’}];
aimFcn=[{@GA},{@PSO},{@AFO3}];
%% 初始化
rng(noRng)
x=ones(option.numAgent,option.dim);
y=ones(option.numAgent,1);
for i=1:option.numAgent
x(i,:)=rand(size(option.lb)).*(option.ub-option.lb)+option.lb;
y(i)=option.fobj(x(i,:),option,data);
end
%% 使用算法求解
bestX=x;
for i=1:length(aimFcn)
rng(noRng)
tic
[bestY(i,:),bestX(i,:),recording(i)]=aimFcn{i}(x,y,option,data);
tt(i)=toc;
end
%% 绘制迭代曲线
figure
hold on
for i=1:length(aimFcn)
if i>1
plot((recording(i).bestFit),’LineWidth’,2)
else
plot((recording(i).bestFit),’–‘,’LineWidth’,2)
end
end
legend(str_legend)
xlabel(‘评价次数（*100）’)
ylabel(‘适应度函数值’)
set(gca,’LooseInset’,get(gca,’TightInset’))
%% 计算结果
for i=1:length(str_legend)
str=[str_legend{i},’优化后方案’];
[~,result(i)]=option.fobj(bestX(i,:),option,data);
% drawPC(result(i),data,str)
end
%%
rng(11)
x0=[0.2273916673684650.8798167565121740.7589780724052870.5108385157912280.1921885362122610.6514412691467130.4189634221944750.5524356630220930.6921786447664800.5251212733372960.9014433037740140.8704339990211020.7279078365750270.6355551507287100.3779484891414700.06657473809005940.3803753221857060.2712951390057370.5563900721172350.8363245942941460.4126505098622440.4354080444621440.3391602212829540.5176730831868580.3051395431634610.7591539282214890.6616510476253650.6956632612060500.5798622433073740.02037687579430830.5675879765621740.4175603151187650.1948655726786590.03550234469248710.06560908820427560.2838226473976960.4093046427225000.7973714666671360.1397341097170420.9435676702110170.7363401447244200.8993021804867180.09584187932385670.4949472204852180.3770430833358390.3920909018258930.8722245882217990.2250189733695530.6584966496095250.9857722428938240.3122429369446740.01767522280381840.1513055015468660.1179204804977460.1373441841899110.9290807460368360.7370560367831240.2645833715934730.9357099728062560.9305421221249210.4583713734982950.02169664090453970.06151249066046290.5690725230797610.9127159796009650.6840164834160110.08729760044239560.8541239898268400.6998918781328570.6032339188826510.6262664661898810.7643576285952050.6339794769559220.1628933417800990.2774110628364120.8392247237757260.9085505381678010.8905867827540620.4663917695029050.4884487502632300.3652366759415880.7302923296773240.7489715713061970.706903738180738];
gs = GlobalSearch;
problem = createOptimProblem(‘fmincon’,’x0′,x0,…
‘objective’,@(x)aimFcn_1(x,option,data),’lb’,x0*0,’ub’,x0*0+1);
x = run(gs,problem);
[fit1,result(i+1)]=aimFcn_1(x,option,data);
%%
P=[
11110.610000000000000
0.610000000000000110.81000000000000000
0.8300000000000000.920000000000000110.9000000000000000.830000000000000
1110.93000000000000010.930000000000000]; %注 P是从result里面复制出来的
str_legend=[str_legend,{‘GS’}];
figure
bar(0:5,P,’grouped’)
legend(str_legend)
xlabel(‘约束’)
ylabel(‘满足概率’)
set(gca,’LooseInset’,get(gca,’TightInset’))
? 运行结果
? 参考文献
[1]何文护. 基于混合遗传算法的多式联运优化的选择研究[D]. 大连海事大学, 2011.
[2]李干亮. 基于多维视角的中新集装箱多式联运路径优化研究[D]. 重庆交通大学, 2019.
? 完整代码??部分理论引用网络文献，若有侵权联系博主删除?? 关注我领取海量matlab电子书和数学建模资料
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