南农工运筹学lingo实验程序结果.docx
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南农工运筹学lingo实验程序结果.docx
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南农工运筹学lingo实验程序结果
实验一:
MIN=X1+X2+X3+X4+X5+X6;
X6+x1>=60;
X1+X2>=70;
X2+X3>=60;
X3+X4>=50;
X4+X5>=20;
X5+X6>=30;
结果:
实验2:
max=9*x1+8*x2+50*x3+19*x4;
3*x1+2*x2+10*x3+4*x4<=18;
2*x3<=3;
灵敏度分析(3、4):
灵敏度分析(5):
max=9*x1+8*x2+50*x3+19*x4+0*x5;
3*x1+2*x2+10*x3+4*x4+3*x5<=18;
2*x3+x5<=3;
(6):
max=9*x1+8*x2+50*x3+19*x4;
3*x1+2*x2+10*x3+4*x4<=18;
2*x3<=3;
4*x1+3*x2+5*x3+2*x4<=8;
实验四运输问题
model:
sets:
warehouse/1..3/:
a;
customer/1..5/:
b;
routes(warehouse,customer):
c,x;
endsets
data:
a=60000,80000,50000;
b=12000,18000,25000,30000,20000;
c=340,380,320,290,310,
280,325,335,250,340,
240,250,310,330,290;
enddata
[obj]min=@sum(routes:
c*x);
@for(warehouse(i):
[sup]@sum(customer(j):
x(i,j))<=a(i));
@for(customer(j):
[dem]@sum(warehouse(i):
x(i,j))=b(j));
end
实验五:
不考虑目标时:
model:
sets:
warehouses/wh1..wh2/:
capacity;
vendors/v1..v3/:
demand;
links(warehouses,vendors):
cost,volume;
endsets
min=@sum(links:
cost*volume);
@for(vendors(j):
@sum(warehouses(i):
volume(i,j))<=demand(j));
@for(warehouses(i):
@sum(vendors(j):
volume(i,j))=capacity(i));
data:
capacity=30004000;
demand=200015005000;
cost=10412
8103;
enddata
end
目标规划模型程序
model:
sets:
level/1..5/:
p,z,goal;
variable/1..6/:
x;
s_con_num/1..5/:
g,dplus,dminus;
s_cons(s_con_num,variable):
c;
obj(level,s_con_num):
wplus,wminus;
endsets
data:
p=10000;
goal=10001000100010000;
g=200011253750330001000;
c=100100
010010
001001
104128103
000100;
wplus=00000
00000
00000
00010
00000;
wminus=10000
01000
00100
00000
00001;
enddata
min=@sum(level:
p*z);
@for(level(i):
z(i)=@sum(s_con_num(j):
wplus(I,j)*dplus(j))+@sum(s_con_num(j):
wminus(I,j)*dminus(j)));
@for(s_con_num(i):
@sum(variable(j):
c(I,j)*x(j))+dminus(i)-dplus(i)=g(i));
@for(level(i)|i#lt#@size(level):
@bnd(0,z(i),goal(i)));
End
第一次P输入1,0,0,0,0;goal输入4个较大的数1000,1000,1000,1000,0的求解结果:
第二次P输入0,1,1,0,0;goal输入3个较大的数0,1000,1000,1000,0的求解结果:
第三次P输入0,0,0,1,0;goal输入1个较大的数0,0,0,1000,0的求解结果:
第四次P输入0,0,0,0,1;goal输入0个较大的数0,0,0,0,0的求解结果:
由lingo数据可得:
四个目标满足的条件是:
客户1:
产地1:
1000+产地2:
1000
客户2:
产地1:
1125
客户3:
产地1:
750+产地2:
3000
且:
产地1输出=1000+1125+750=2875<3000(满足)
产地2输出=1000+3000=4000<=4000(满足)
第一目标:
1000+1000=2000(满足)
第二目标:
1125/1500*100%=75%(满足)
(750+3000)/5000=75%(满足)
实验6总余量最少
min=3*x1+x2+3*x3+3*x4+x5+x6+3*x7;
4*x1+3*x2+2*x3+x4+x5>=50;
x2+2*x4+x5+3*x6>=20;
x3+x5+2*x7>=15;
@gin(x1);
@gin(x2);
@gin(x3);
@gin(x4);
@gin(x5);
@gin(x5);
@gin(x6);
@gin(x7);
总根数最少
min=x1+x2+x3+x4+x5+x6+x7;
4*x1+3*x2+2*x3+x4+x5>=50;
x2+2*x4+x5+3*x6>=20;
x3+x5+2*x7>=15;
@gin(x1);
@gin(x2);
@gin(x3);
@gin(x4);
@gin(x5);
@gin(x5);
@gin(x6);
@gin(x7);
min=x1+x2+x3;
x1*r11+x2*r12+x3*r13>=50;
x1*r21+x2*r22+x3*r23>=10;
x1*r31+x2*r32+x3*r33>=20;
x1*r41+x2*r42+x3*r43>=15;
4*r11+5*r21+6*r31+8*r41<=19;
4*r12+5*r22+6*r32+8*r42<=19;
4*r13+5*r23+6*r33+8*r43<=19;
4*r11+5*r21+6*r31+8*r41>=16;
4*r12+5*r22+6*r32+8*r42>=16;
4*r13+5*r23+6*r33+8*r43>=16;
x1+x2+x3>=26;
x1+x2+x3<=31;
x1>=x2;
x2>=x3;
@gin(x1);@gin(x2);@gin(x3);@gin(r11);@gin(r12);@gin(r13);@gin(r21);@gin(r22);@gin(r23);@gin(r31);@gin(r32);@gin(r33);@gin(r41);@gin(r42);@gin(r43);
End
实验七:
图与网络分析
2.
model:
sets:
places/A,B1,B2,C1,C2,C3,C4,D1,D2,D3,E1,E2,F/:
L;
roads(places,places)/A,B1A,B2B1,C1B1,C2B1,C3B2,C2B2,C3B2,C4C1,D1C1,D2C2,D1C2,D2C3,D2C3,D3C4,D2C4,D3D1,E1D1,E2D2,E1D2,E2D3,E1D3,E2E1,FE2,F/:
d;
endsets
data:
D=452368775845348435621343;
L=0,,,,,,,,,,,,;
enddata
@for(places(I)|I#GT#@index(A):
L(I)=@min(roads(J,I):
L(J)+D(J,I)););
End
(2)
3.
(2)
sets:
nodes/s,1,2,3,4,t/;
arcs(nodes,nodes)/s,1s,21,21,32,43,23,t4,34,t/:
c,f;
endsets
data:
c=8759925610;
enddata
max=flow;
@sum(arcs(I,j)|i#eq#1:
f(I,j))=flow;
@for(nodes(i)|i#ne#1#and#i#ne#@size(nodes):
@sum(arcs(I,j):
f(I,j))-@sum(arcs(j,i):
f(j,i))=0;
@for(arcs:
@bnd(0,f,c)));
@for(arcs(i,j):
f(i,j)<=c(i,j));
End
求解结果:
实验八动态规划
Localoptimalsolutionfound.
Objectivevalue:
200.0000
Infeasibilities:
0.000000
Extendedsolversteps:
5
Totalsolveriterations:
46
VariableValueReducedCost
X10.00000036.00000
X20.00000031.00000
X310.000000.000000
RowSlackorSurplusDualPrice
1200.00001.000000
20.00000040.00000
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