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⛄ 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=[0.227391667368465 0.879816756512174 0.758978072405287 0.510838515791228 0.192188536212261 0.651441269146713 0.418963422194475 0.552435663022093 0.692178644766480 0.525121273337296 0.901443303774014 0.870433999021102 0.727907836575027 0.635555150728710 0.377948489141470 0.0665747380900594 0.380375322185706 0.271295139005737 0.556390072117235 0.836324594294146 0.412650509862244 0.435408044462144 0.339160221282954 0.517673083186858 0.305139543163461 0.759153928221489 0.661651047625365 0.695663261206050 0.579862243307374 0.0203768757943083 0.567587976562174 0.417560315118765 0.194865572678659 0.0355023446924871 0.0656090882042756 0.283822647397696 0.409304642722500 0.797371466667136 0.139734109717042 0.943567670211017 0.736340144724420 0.899302180486718 0.0958418793238567 0.494947220485218 0.377043083335839 0.392090901825893 0.872224588221799 0.225018973369553 0.658496649609525 0.985772242893824 0.312242936944674 0.0176752228038184 0.151305501546866 0.117920480497746 0.137344184189911 0.929080746036836 0.737056036783124 0.264583371593473 0.935709972806256 0.930542122124921 0.458371373498295 0.0216966409045397 0.0615124906604629 0.569072523079761 0.912715979600965 0.684016483416011 0.0872976004423956 0.854123989826840 0.699891878132857 0.603233918882651 0.626266466189881 0.764357628595205 0.633979476955922 0.162893341780099 0.277411062836412 0.839224723775726 0.908550538167801 0.890586782754062 0.466391769502905 0.488448750263230 0.365236675941588 0.730292329677324 0.748971571306197 0.706903738180738];
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=[
0.610000000000000 1 1 1 1 0.610000000000000
0.610000000000000 1 1 0.810000000000000 0 0
0.830000000000000 0.920000000000000 1 1 0.900000000000000 0.830000000000000
1 1 1 0.930000000000000 1 0.930000000000000]; %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.
