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)));
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@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
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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
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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
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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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