74 0.000000 75 0.000000 76 0.000000 77 0.000000 78 0.000000 79 0.000000 80 0.000000 81 0.000000 82 0.000000 83 0.000000 84 0.000000 85 0.000000 86 0.000000 87 0.000000 88 0.000000 89 0.000000 90 0.000000 91 0.000000 92 0.000000 2）问题二的求解
1将模型编写的LINGO程序语句 ○
sets:
time/1..7/:Q,T;
mode/1..4/:S,E,F,N,A,B; link(time,mode):P,X,K,c,d; endsets data:
Q=12000,32000,25000,36000,25000,30000,18000; T=6,3,3,2,4,4,2;
S=5000,1600,2400,1200; E=2250,1800,3750,4800; F=2.7,2.2,1.8,3.8; N=10,4,8,3;
A=750,1000,1200,1800; B=1750,1500,2000,3500; enddata
min=@sum(link(i,j):(X(i,j)-c(i,j))*S(j)*K(i,j)+X(i,j)*T(i)*(E(j)+(P(i,j)-A(j))*F(j)));
@for(link(i,j):c=@if(i#GE#2,X(i-1,j),X(7,j))); @for(link(i,j):P(i,j)=@if(X(i,j)#GT#0,d(i,j),0));
@for(time(i):@for(mode(j):@bnd(A(j),d(i,j),B(j)*0.8)));
8
@for(time(i):(@sum(mode(j):P(i,j)*X(i,j)))>=Q(i)); @for(link(i,j):K(i,j)=@if(X(i,j)#GE#c(i,j),1,0)); @for(link(i,j):@gin(X(i,j)));
@for(time(i):@for(mode(j):@bnd(0,X(i,j),N(j)))); END
○
2LINGO对上述模型的程序运行结果 Local optimal solution found.
Objective value: 1448700. Objective bound: 1410307. Infeasibilities: 0.000000 Extended solver steps: 69 Total solver iterations: 54752211
Variable Value Q( 1) 12000.00 Q( 2) 32000.00 Q( 3) 25000.00 Q( 4) 36000.00 Q( 5) 25000.00 Q( 6) 30000.00 Q( 7) 18000.00 T( 1) 6.000000 T( 2) 3.000000 T( 3) 3.000000 T( 4) 2.000000 T( 5) 4.000000 T( 6) 4.000000 T( 7) 2.000000 S( 1) 5000.000 S( 2) 1600.000 S( 3) 2400.000 S( 4) 1200.000 E( 1) 2250.000 E( 2) 1800.000 E( 3) 3750.000 E( 4) 4800.000 F( 1) 2.700000 F( 2) 2.200000 F( 3) 1.800000
9
F( 4) 3.800000 N( 1) 10.00000 N( 2) 4.000000 N( 3) 8.000000 N( 4) 3.000000 A( 1) 750.0000 A( 2) 1000.000 A( 3) 1200.000 A( 4) 1800.000 B( 1) 1750.000 B( 2) 1500.000 B( 3) 2000.000 B( 4) 3500.000 P( 1, 1) 750.0000 P( 1, 2) 1125.000 P( 1, 3) 2000.000 P( 1, 4) 0.000000 P( 2, 1) 1750.000 P( 2, 2) 1500.000 P( 2, 3) 2000.000 P( 2, 4) 2166.667 P( 3, 1) 750.0000 P( 3, 2) 1425.000 P( 3, 3) 2000.000 P( 3, 4) 1800.000 P( 4, 1) 1750.000 P( 4, 2) 1500.000 P( 4, 3) 2000.000 P( 4, 4) 3500.000 P( 5, 1) 750.0000 P( 5, 2) 1425.000 P( 5, 3) 2000.000 P( 5, 4) 1800.000 P( 6, 1) 1300.000 P( 6, 2) 1500.000 P( 6, 3) 2000.000 P( 6, 4) 1800.000 P( 7, 1) 1200.000 P( 7, 2) 1500.000 P( 7, 3) 2000.000 P( 7, 4) 1800.000 X( 1, 1) 2.000000
10
X( 1, 2) 4.000000 X( 1, 3) 3.000000 X( 1, 4) 0.000000 X( 2, 1) 2.000000 X( 2, 2) 4.000000 X( 2, 3) 8.000000 X( 2, 4) 3.000000 X( 3, 1) 2.000000 X( 3, 2) 4.000000 X( 3, 3) 8.000000 X( 3, 4) 1.000000 X( 4, 1) 2.000000 X( 4, 2) 4.000000 X( 4, 3) 8.000000 X( 4, 4) 3.000000 X( 5, 1) 2.000000 X( 5, 2) 4.000000 X( 5, 3) 8.000000 X( 5, 4) 1.000000 X( 6, 1) 2.000000 X( 6, 2) 4.000000 X( 6, 3) 8.000000 X( 6, 4) 3.000000 X( 7, 1) 2.000000 X( 7, 2) 4.000000 X( 7, 3) 3.000000 X( 7, 4) 2.000000 K( 1, 1) 1.000000 K( 1, 2) 1.000000 K( 1, 3) 1.000000 K( 1, 4) 0.000000 K( 2, 1) 1.000000 K( 2, 2) 1.000000 K( 2, 3) 1.000000 K( 2, 4) 1.000000 K( 3, 1) 1.000000 K( 3, 2) 1.000000 K( 3, 3) 1.000000 K( 3, 4) 0.000000 K( 4, 1) 1.000000 K( 4, 2) 1.000000 K( 4, 3) 1.000000
11
K( 4, 4) 1.000000 K( 5, 1) 1.000000 K( 5, 2) 1.000000 K( 5, 3) 1.000000 K( 5, 4) 0.000000 K( 6, 1) 1.000000 K( 6, 2) 1.000000 K( 6, 3) 1.000000 K( 6, 4) 1.000000 K( 7, 1) 1.000000 K( 7, 2) 1.000000 K( 7, 3) 0.000000 K( 7, 4) 0.000000 C( 1, 1) 2.000000 C( 1, 2) 4.000000 C( 1, 3) 3.000000 C( 1, 4) 2.000000 C( 2, 1) 2.000000 C( 2, 2) 4.000000 C( 2, 3) 3.000000 C( 2, 4) 0.000000 C( 3, 1) 2.000000 C( 3, 2) 4.000000 C( 3, 3) 8.000000 C( 3, 4) 3.000000 C( 4, 1) 2.000000 C( 4, 2) 4.000000 C( 4, 3) 8.000000 C( 4, 4) 1.000000 C( 5, 1) 2.000000 C( 5, 2) 4.000000 C( 5, 3) 8.000000 C( 5, 4) 3.000000 C( 6, 1) 2.000000 C( 6, 2) 4.000000 C( 6, 3) 8.000000 C( 6, 4) 1.000000 C( 7, 1) 2.000000 C( 7, 2) 4.000000 C( 7, 3) 8.000000 C( 7, 4) 3.000000 D( 1, 1) 750.0000
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