【车间调度】基于卷积神经网络的柔性作业车间调度问题的两阶段算法（Matlab代码实现）
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本文目录如下：
目录
1 概述
2 运行结果
3 Matlab代码实现
4 参考文献
1 概述
该文提出一种基于卷积神经网络的有效两阶段算法，以求解具有机器故障的柔性作业车间调度问题（FJSP）。建立了以最大完成时间和鲁棒性为目标的双目标动态柔性作业车间调度问题（DFJSP）模型。提出了CNN构建的预测模型，并开发了一个名为RMn的替代指标来评估鲁棒性。实验结果表明，所提两阶段算法对求解DFJSP有效，RMn能够更快、更高效、更准确地评估调度的鲁棒性。
2 运行结果
部分代码：
function [makespan1,RMn]=caltimen(S,pop)
Jm=pop.Jm;
T=pop.T;
PNumber=pop.PNumber;
MPNumber=pop.MPNumber;
JmNumber=pop.JmNumber;
WNumber=pop.WNumber;
Number=pop.Number;
Number_s = cumsum(Number);
NINDA=1;
NINDB=0;
%%
S1=S(1,1:WNumber);
MU=zeros(PNumber,MPNumber);
PJT=zeros(PNumber,MPNumber);
s=1;
for i=1:PNumber
val=Number(i);
for j=1:val
M=S1(1,s);
s=s+1;
Temp1=Jm{i,j};
m=Temp1(M);
MU(i,j)=m;
Temp2=T{i,j};
t=Temp2(M);
PJT(i,j)=t;
end
end
%%
S2=S(1,(1+WNumber):2*WNumber);
temp=zeros(1,PNumber);
P=zeros(1,WNumber);
for i=1:WNumber
temp(S2(i))=temp(S2(i))+1;
P(i)=S2(i)*100+temp(S2(i));
end
%%
TM=zeros(1,JmNumber);
TP=zeros(1,PNumber);
PVal=zeros(2,WNumber);
t1=zeros(JmNumber,WNumber);
t2=zeros(JmNumber,WNumber);
TSE=zeros(5*JmNumber,WNumber);
MPN=zeros(1,JmNumber);
%%
for ii=1:WNumber
if ii==1
PNval=P(1,ii);
a=(mod(PNval,100));
b=((PNval-a)/100);
m=MU(b,a);
t=PJT(b,a);
TMval=TM(1,m);
TPval=TP(1,b);
if TMval>TPval
val=TMval;
else
val=TPval;
end
PVal(1,ii)=val;
PVal(2,ii)=val+t;
TM(1,m)=PVal(2,ii);
TP(1,b)=PVal(2,ii);
MPN(1,m)=MPN(1,m)+1;
t1(m,ii)=TM(1,m)-t;
t2(m,ii)=TM(1,m);
TSE(5*m-4,1)=PNval;
TSE(5*m-3,1)=PVal(1,1);
TSE(5*m-2,1)=PVal(2,1);
TSE(5*m-1,1)=0;
TSE(5*m-0,1)=PVal(1,1);
else
PNval=P(1,ii);
a=(mod(PNval,100));
b=((PNval-a)/100);
m=MU(b,a);
t=PJT(b,a);
TMval=TM(1,m);
TPval=TP(1,b);
TSEok=0;
%%
for w=1:MPN(1,m)
TSval=TSE(5*m-1,w);
TEval=TSE(5*m,w);
SJM=max(TSval,TPval);
if (SJM+t<=TEval)
TSEok=1;
if TSval<=TPval
PVal(1,ii)=TPval;
PVal(2,ii)=TPval+t;
t1(m,ii)=PVal(2,ii)-t;
t2(m,ii)=PVal(2,ii);
TP(1,b)=PVal(2,ii);
MPN(1,m)=MPN(1,m)+1;
TSE(5*m-4:5*m,w+1:WNumber)=TSE(5*m-4:5*m,w:WNumber-1);
TSE(5*m-4,w)=PNval;
TSE(5*m-3,w)=PVal(1,ii);
TSE(5*m-2,w)=PVal(2,ii);
if w-1==0
TSE(5*m-1,w)=0;
else
TSE(5*m-1,w)=TSE(5*m-2,w-1);
end
TSE(5*m,w)=PVal(1,ii);
TSE(5*m-1,w+1)=PVal(2,ii);
else
PVal(1,ii)=TSval;
PVal(2,ii)=TSval+t;
t1(m,ii)=PVal(2,ii)-t;
t2(m,ii)=PVal(2,ii);
TP(1,b)=PVal(2,ii);
MPN(1,m)=MPN(1,m)+1;
TSE(5*m-4:5*m,w+1:WNumber)=TSE(5*m-4:5*m,w:WNumber-1);
TSE(5*m-4,w)=PNval;
TSE(5*m-3,w)=PVal(1,ii);
TSE(5*m-2,w)=PVal(2,ii);
if w-1==0
TSE(5*m-1,w)=0;
else
TSE(5*m-1,w)=TSE(5*m-2,w-1);
end
TSE(5*m,w)=PVal(1,ii);
TSE(5*m-1,w+1)=PVal(2,ii);
end
end
if TSEok==1
break
end
end
if TSEok~=1
if TMval<=TPval
PVal(1,ii)=TPval;
PVal(2,ii)=TPval+t;
TM(1,m)=PVal(2,ii);
TP(1,b)=PVal(2,ii);
MPN(1,m)=MPN(1,m)+1;
t1(m,ii)=TM(1,m)-t;
t2(m,ii)=TM(1,m);
TSE(5*m-4,MPN(1,m))=PNval;
TSE(5*m-3,MPN(1,m))=PVal(1,ii);
TSE(5*m-2,MPN(1,m))=PVal(2,ii);
if MPN(1,m)==1
TSE(5*m-1,MPN(1,m))=0;
else
TSE(5*m-1,MPN(1,m))=TSE(5*m-2,MPN(1,m)-1);
end
TSE(5*m,MPN(1,m))=PVal(1,ii);
else
PVal(1,ii)=TMval;
PVal(2,ii)=TMval+t;
TM(1,m)=PVal(2,ii);
TP(1,b)=PVal(2,ii);
MPN(1,m)=MPN(1,m)+1;
t1(m,ii)=TM(1,m)-t;
t2(m,ii)=TM(1,m);
TSE(5*m-4,MPN(1,m))=PNval;
TSE(5*m-3,MPN(1,m))=PVal(1,ii);
TSE(5*m-2,MPN(1,m))=PVal(2,ii);
if MPN(1,m)==1
TSE(5*m-1,MPN(1,m))=0;
else
TSE(5*m-1,MPN(1,m))=TSE(5*m-2,MPN(1,m)-1);
end
TSE(5*m,MPN(1,m))=PVal(1,ii);
end
end
end
end
makespan1=max(max(PVal));
%% float time ftc
TSE_1 = zeros(3*JmNumber,WNumber);
TSE_2 = zeros(4,WNumber);
kongxitime1=zeros(2,WNumber);
Tlates = zeros(2,WNumber);
ftc = zeros(2,WNumber);
Avecft = zeros(2,WNumber);
T = PVal(2,:)-PVal(1,:);
earlytime = [P;PVal(1,:);T];
earlytime = sortrows(earlytime')';
b1 = zeros(1,JmNumber);
for i = 1:JmNumber
TSE1 = TSE(5*i-4,:);
a1 = find(TSE1==0);
b1(i) = a1(1)-1;
end
for k = 1:JmNumber
for w = 1:b1(k)-1
TSE_1(3*k-2,w)=TSE(5*k-4,w);
TSE_1(3*k-1,w)=TSE(5*k-1,w+1);
TSE_1(3*k,w)=TSE(5*k,w+1);
TSE_1(3*k-2,b1(k))=TSE(5*k-4,b1(k));
TSE_1(3*k-1,b1(k))=TSE(5*k-2,b1(k));
TSE_1(3*k,b1(k))=makespan1;
end
end
b1_S = cumsum(b1);
b1_S = [0,b1_S];
for j = 1:JmNumber
TSE_2(1,b1_S(j)+1:b1_S(j+1)) = TSE_1(3*j-2,1:b1(j));
TSE_2(2,b1_S(j)+1:b1_S(j+1)) = TSE_1(3*j-1,1:b1(j));
TSE_2(3,b1_S(j)+1:b1_S(j+1)) = TSE_1(3*j,1:b1(j));
end
TSE_2 = sortrows(TSE_2')';
TSE_2(4,:) = TSE_2(3,:)-TSE_2(2,:);
Tlates(1,:) = TSE_2(1,:);
for n=1:WNumber
if TSE_2(4,n)==0
Tlates(2,n) = earlytime(2,n);
else
if ismember(n,Number_s)==1
kongxitime1(1,n) = TSE_2(1,n);
kongxitime1(2,n) = TSE_2(4,n);
else
if TSE_2(3,n)<= earlytime(2,n+1)
Tlates(2,n) = TSE_2(3,n)-earlytime(3,n);
else
Tlates(2,n) = earlytime(2,n+1)-earlytime(3,n);
end
end
end
end
ftc(1,:) = Tlates(1,:);
ftc(2,:) = Tlates(2,:)-earlytime(2,:);
col=find(kongxitime1(1,:)~=0);
for m=1:length(col)
ftc(1,col(m)) = kongxitime1(1,col(m));
ftc(2,col(m)) = kongxitime1(2,col(m));
end
Avecft(1,:) = ftc(1,:);
xulie=find(ftc(2,:)~=0);
for r=1:length(xulie)
Avecft(2,xulie(r)) = TSE_2(2,xulie(r))+ftc(2,xulie(r))/2;
end
%%
Wc = reshape(PJT',1,pop.PNumber*pop.MPNumber);
Wc(find(Wc==0))=[];
Wtot = sum(Wc);
ejc1 = Wc./Wtot.*ftc(2,:);
%% Simulate machine breakdown
Tbusy = sum(PJT');
Pbk=Tbusy/Wtot;
jiqi_0=find(Pbk==max(Pbk(:)));
jiqi=jiqi_0(1);
a1=0.5; %a1=0;
a2=1; %a2=0.5;
b1=0.35; %b1=0.1;
b2=0.4; %b2=0.15;
ts=unifrnd(a1*makespan1,a2*makespan1,1,1);
ts=roundn(ts,0);
te=unifrnd(b1*Tbusy(jiqi),b2*Tbusy(jiqi),1,1);
te=roundn(te,0);
%% Calculate the objective function value
Avekcmb=te/2+ts;
ejc2=(1./exp(abs(Avecft(2,:)-Avekcmb))).*ftc(2,:);
ej=[ejc1,ejc2];
%% Enter a predictive model
load ('net1-10-10.mat')
pt=ej';
t1=sim(net,pt);
RMn=t1';
end
3 Matlab代码实现
4 参考文献
部分理论来源于网络，如有侵权请联系删除。
[1]Guohui Zhang, Xixi Lu, Xing Liu, Litao Zhang, Shiwen Wei, Wenqiang Zhang (2022) An two-stage algorithm based on convolutional neural network for flexible job shop scheduling problem