运筹学运输问题 发表于 2018-11-28 | 阅读次数: 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611Exam2model:sets:Warehouse/1..3/:a;Customer/1..4/:b;Routes(Warehouse,Customer):c,x;endsetsdata:a=7,5,7;b=2,3,4,6;c=2,11,3,4,10,3,5,9,7,8,1,2;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 Global optimal solution found. Objective value: 35.00000 Infeasibilities: 0.000000 Total solver iterations: 5 Variable Value Reduced Cost A( 1) 7.000000 0.000000 A( 2) 5.000000 0.000000 A( 3) 7.000000 0.000000 B( 1) 2.000000 0.000000 B( 2) 3.000000 0.000000 B( 3) 4.000000 0.000000 B( 4) 6.000000 0.000000 C( 1, 1) 2.000000 0.000000 C( 1, 2) 11.00000 0.000000 C( 1, 3) 3.000000 0.000000 C( 1, 4) 4.000000 0.000000 C( 2, 1) 10.00000 0.000000 C( 2, 2) 3.000000 0.000000 C( 2, 3) 5.000000 0.000000 C( 2, 4) 9.000000 0.000000 C( 3, 1) 7.000000 0.000000 C( 3, 2) 8.000000 0.000000 C( 3, 3) 1.000000 0.000000 C( 3, 4) 2.000000 0.000000 X( 1, 1) 2.000000 0.000000 X( 1, 2) 0.000000 8.000000 X( 1, 3) 0.000000 0.000000 X( 1, 4) 3.000000 0.000000 X( 2, 1) 0.000000 8.000000 X( 2, 2) 3.000000 0.000000 X( 2, 3) 0.000000 2.000000 X( 2, 4) 0.000000 5.000000 X( 3, 1) 0.000000 7.000000 X( 3, 2) 0.000000 7.000000 X( 3, 3) 4.000000 0.000000 X( 3, 4) 3.000000 0.000000 Row Slack or Surplus Dual Price OBJ 35.00000 -1.000000 SUP( 1) 2.000000 0.000000 SUP( 2) 2.000000 0.000000 SUP( 3) 0.000000 2.000000 DEM( 1) 0.000000 -2.000000 DEM( 2) 0.000000 -3.000000 DEM( 3) 0.000000 -3.000000 DEM( 4) 0.000000 -4.000000Exam3model:sets:Warehouse/1..3/:a;Customer/1..4/:b,D;Routes(Warehouse,Customer):c,x;endsetsdata:a=50,60,50;b=30,70,0,10;d=50,70,30,1000000;c=16,13,22,17,14,13,19,15,19,20,23,1000000;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));@for(Customer(j):[FHS]@sum(Warehouse(i):x(i,j))<=d(j));end Global optimal solution found. Objective value: 1480.000 Infeasibilities: 0.000000 Total solver iterations: 4 Variable Value Reduced Cost A( 1) 50.00000 0.000000 A( 2) 60.00000 0.000000 A( 3) 50.00000 0.000000 B( 1) 30.00000 0.000000 B( 2) 70.00000 0.000000 B( 3) 0.000000 0.000000 B( 4) 10.00000 0.000000 D( 1) 50.00000 0.000000 D( 2) 70.00000 0.000000 D( 3) 30.00000 0.000000 D( 4) 1000000. 0.000000 C( 1, 1) 16.00000 0.000000 C( 1, 2) 13.00000 0.000000 C( 1, 3) 22.00000 0.000000 C( 1, 4) 17.00000 0.000000 C( 2, 1) 14.00000 0.000000 C( 2, 2) 13.00000 0.000000 C( 2, 3) 19.00000 0.000000 C( 2, 4) 15.00000 0.000000 C( 3, 1) 19.00000 0.000000 C( 3, 2) 20.00000 0.000000 C( 3, 3) 23.00000 0.000000 C( 3, 4) 1000000. 0.000000 X( 1, 1) 0.000000 2.000000 X( 1, 2) 50.00000 0.000000 X( 1, 3) 0.000000 22.00000 X( 1, 4) 0.000000 2.000000 X( 2, 1) 30.00000 0.000000 X( 2, 2) 20.00000 0.000000 X( 2, 3) 0.000000 19.00000 X( 2, 4) 10.00000 0.000000 X( 3, 1) 0.000000 5.000000 X( 3, 2) 0.000000 7.000000 X( 3, 3) 0.000000 23.00000 X( 3, 4) 0.000000 999985.0 Row Slack or Surplus Dual Price OBJ 1480.000 -1.000000 SUP( 1) 0.000000 0.000000 SUP( 2) 0.000000 0.000000 SUP( 3) 50.00000 0.000000 DEM( 1) 0.000000 -14.00000 DEM( 2) 0.000000 -13.00000 DEM( 3) 0.000000 0.000000 DEM( 4) 0.000000 -15.00000 FHS( 1) 20.00000 0.000000 FHS( 2) 0.000000 0.000000 FHS( 3) 30.00000 0.000000 FHS( 4) 999990.0 0.000000Exam4model:sets:Warehouse/1..11/:a;Customer/1..11/:b;Routes(Warehouse,Customer):c,x;endsetsdata:a=27,24,29,20,20,20,20,20,20,20,20;b=20,20,20,20,20,20,20,23,26,25,26;c=0,1,3,2,1,4,3,3,11,3,10,1,0,1000000000,3,5,100000000,2,1,9,2,8,3,100000000,0,1,10000000,2,3,7,4,10,5,2,3,1,0,1,3,2,2,8,4,6,1,5,10000000,1,0,1,1,4,5,2,7,4,10000,2,3,1,0,2,1,8,2,4,3,2,3,2,1,2,0,1,100000,2,6,3,1,7,2,4,1,1,0,1,4,2,11,9,4,8,5,8,10000,1,0,2,1,3,2,10,4,2,2,2,4,2,0,8,10,8,5,6,7,4,6,2,1,8,0;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 Global optimal solution found. Objective value: 68.00000 Infeasibilities: 0.000000 Total solver iterations: 22 Variable Value Reduced Cost A( 1) 27.00000 0.000000 A( 2) 24.00000 0.000000 A( 3) 29.00000 0.000000 A( 4) 20.00000 0.000000 A( 5) 20.00000 0.000000 A( 6) 20.00000 0.000000 A( 7) 20.00000 0.000000 A( 8) 20.00000 0.000000 A( 9) 20.00000 0.000000 A( 10) 20.00000 0.000000 A( 11) 20.00000 0.000000 B( 1) 20.00000 0.000000 B( 2) 20.00000 0.000000 B( 3) 20.00000 0.000000 B( 4) 20.00000 0.000000 B( 5) 20.00000 0.000000 B( 6) 20.00000 0.000000 B( 7) 20.00000 0.000000 B( 8) 23.00000 0.000000 B( 9) 26.00000 0.000000 B( 10) 25.00000 0.000000 B( 11) 26.00000 0.000000 C( 1, 1) 0.000000 0.000000 C( 1, 2) 1.000000 0.000000 C( 1, 3) 3.000000 0.000000 C( 1, 4) 2.000000 0.000000 C( 1, 5) 1.000000 0.000000 C( 1, 6) 4.000000 0.000000 C( 1, 7) 3.000000 0.000000 C( 1, 8) 3.000000 0.000000 C( 1, 9) 11.00000 0.000000 C( 1, 10) 3.000000 0.000000 C( 1, 11) 10.00000 0.000000 C( 2, 1) 1.000000 0.000000 C( 2, 2) 0.000000 0.000000 C( 2, 3) 0.1000000E+10 0.000000 C( 2, 4) 3.000000 0.000000 C( 2, 5) 5.000000 0.000000 C( 2, 6) 0.1000000E+09 0.000000 C( 2, 7) 2.000000 0.000000 C( 2, 8) 1.000000 0.000000 C( 2, 9) 9.000000 0.000000 C( 2, 10) 2.000000 0.000000 C( 2, 11) 8.000000 0.000000 C( 3, 1) 3.000000 0.000000 C( 3, 2) 0.1000000E+09 0.000000 C( 3, 3) 0.000000 0.000000 C( 3, 4) 1.000000 0.000000 C( 3, 5) 0.1000000E+08 0.000000 C( 3, 6) 2.000000 0.000000 C( 3, 7) 3.000000 0.000000 C( 3, 8) 7.000000 0.000000 C( 3, 9) 4.000000 0.000000 C( 3, 10) 10.00000 0.000000 C( 3, 11) 5.000000 0.000000 C( 4, 1) 2.000000 0.000000 C( 4, 2) 3.000000 0.000000 C( 4, 3) 1.000000 0.000000 C( 4, 4) 0.000000 0.000000 C( 4, 5) 1.000000 0.000000 C( 4, 6) 3.000000 0.000000 C( 4, 7) 2.000000 0.000000 C( 4, 8) 2.000000 0.000000 C( 4, 9) 8.000000 0.000000 C( 4, 10) 4.000000 0.000000 C( 4, 11) 6.000000 0.000000 C( 5, 1) 1.000000 0.000000 C( 5, 2) 5.000000 0.000000 C( 5, 3) 0.1000000E+08 0.000000 C( 5, 4) 1.000000 0.000000 C( 5, 5) 0.000000 0.000000 C( 5, 6) 1.000000 0.000000 C( 5, 7) 1.000000 0.000000 C( 5, 8) 4.000000 0.000000 C( 5, 9) 5.000000 0.000000 C( 5, 10) 2.000000 0.000000 C( 5, 11) 7.000000 0.000000 C( 6, 1) 4.000000 0.000000 C( 6, 2) 10000.00 0.000000 C( 6, 3) 2.000000 0.000000 C( 6, 4) 3.000000 0.000000 C( 6, 5) 1.000000 0.000000 C( 6, 6) 0.000000 0.000000 C( 6, 7) 2.000000 0.000000 C( 6, 8) 1.000000 0.000000 C( 6, 9) 8.000000 0.000000 C( 6, 10) 2.000000 0.000000 C( 6, 11) 4.000000 0.000000 C( 7, 1) 3.000000 0.000000 C( 7, 2) 2.000000 0.000000 C( 7, 3) 3.000000 0.000000 C( 7, 4) 2.000000 0.000000 C( 7, 5) 1.000000 0.000000 C( 7, 6) 2.000000 0.000000 C( 7, 7) 0.000000 0.000000 C( 7, 8) 1.000000 0.000000 C( 7, 9) 100000.0 0.000000 C( 7, 10) 2.000000 0.000000 C( 7, 11) 6.000000 0.000000 C( 8, 1) 3.000000 0.000000 C( 8, 2) 1.000000 0.000000 C( 8, 3) 7.000000 0.000000 C( 8, 4) 2.000000 0.000000 C( 8, 5) 4.000000 0.000000 C( 8, 6) 1.000000 0.000000 C( 8, 7) 1.000000 0.000000 C( 8, 8) 0.000000 0.000000 C( 8, 9) 1.000000 0.000000 C( 8, 10) 4.000000 0.000000 C( 8, 11) 2.000000 0.000000 C( 9, 1) 11.00000 0.000000 C( 9, 2) 9.000000 0.000000 C( 9, 3) 4.000000 0.000000 C( 9, 4) 8.000000 0.000000 C( 9, 5) 5.000000 0.000000 C( 9, 6) 8.000000 0.000000 C( 9, 7) 10000.00 0.000000 C( 9, 8) 1.000000 0.000000 C( 9, 9) 0.000000 0.000000 C( 9, 10) 2.000000 0.000000 C( 9, 11) 1.000000 0.000000 C( 10, 1) 3.000000 0.000000 C( 10, 2) 2.000000 0.000000 C( 10, 3) 10.00000 0.000000 C( 10, 4) 4.000000 0.000000 C( 10, 5) 2.000000 0.000000 C( 10, 6) 2.000000 0.000000 C( 10, 7) 2.000000 0.000000 C( 10, 8) 4.000000 0.000000 C( 10, 9) 2.000000 0.000000 C( 10, 10) 0.000000 0.000000 C( 10, 11) 8.000000 0.000000 C( 11, 1) 10.00000 0.000000 C( 11, 2) 8.000000 0.000000 C( 11, 3) 5.000000 0.000000 C( 11, 4) 6.000000 0.000000 C( 11, 5) 7.000000 0.000000 C( 11, 6) 4.000000 0.000000 C( 11, 7) 6.000000 0.000000 C( 11, 8) 2.000000 0.000000 C( 11, 9) 1.000000 0.000000 C( 11, 10) 8.000000 0.000000 C( 11, 11) 0.000000 0.000000 X( 1, 1) 20.00000 0.000000 X( 1, 2) 7.000000 0.000000 X( 1, 3) 0.000000 4.000000 X( 1, 4) 0.000000 2.000000 X( 1, 5) 0.000000 0.000000 X( 1, 6) 0.000000 3.000000 X( 1, 7) 0.000000 2.000000 X( 1, 8) 0.000000 1.000000 X( 1, 9) 0.000000 8.000000 X( 1, 10) 0.000000 0.000000 X( 1, 11) 0.000000 6.000000 X( 2, 1) 0.000000 2.000000 X( 2, 2) 13.00000 0.000000 X( 2, 3) 0.000000 0.1000000E+10 X( 2, 4) 0.000000 4.000000 X( 2, 5) 0.000000 5.000000 X( 2, 6) 0.000000 0.1000000E+09 X( 2, 7) 0.000000 2.000000 X( 2, 8) 6.000000 0.000000 X( 2, 9) 0.000000 7.000000 X( 2, 10) 5.000000 0.000000 X( 2, 11) 0.000000 5.000000 X( 3, 1) 0.000000 2.000000 X( 3, 2) 0.000000 0.1000000E+09 X( 3, 3) 20.00000 0.000000 X( 3, 4) 0.000000 0.000000 X( 3, 5) 0.000000 9999998. X( 3, 6) 0.000000 0.000000 X( 3, 7) 0.000000 1.000000 X( 3, 8) 0.000000 4.000000 X( 3, 9) 9.000000 0.000000 X( 3, 10) 0.000000 6.000000 X( 3, 11) 0.000000 0.000000 X( 4, 1) 0.000000 2.000000 X( 4, 2) 0.000000 2.000000 X( 4, 3) 0.000000 2.000000 X( 4, 4) 20.00000 0.000000 X( 4, 5) 0.000000 0.000000 X( 4, 6) 0.000000 2.000000 X( 4, 7) 0.000000 1.000000 X( 4, 8) 0.000000 0.000000 X( 4, 9) 0.000000 5.000000 X( 4, 10) 0.000000 1.000000 X( 4, 11) 0.000000 2.000000 X( 5, 1) 0.000000 2.000000 X( 5, 2) 0.000000 5.000000 X( 5, 3) 0.000000 0.1000000E+08 X( 5, 4) 0.000000 2.000000 X( 5, 5) 20.00000 0.000000 X( 5, 6) 0.000000 1.000000 X( 5, 7) 0.000000 1.000000 X( 5, 8) 0.000000 3.000000 X( 5, 9) 0.000000 3.000000 X( 5, 10) 0.000000 0.000000 X( 5, 11) 0.000000 4.000000 X( 6, 1) 0.000000 5.000000 X( 6, 2) 0.000000 10000.00 X( 6, 3) 0.000000 4.000000 X( 6, 4) 0.000000 4.000000 X( 6, 5) 0.000000 1.000000 X( 6, 6) 20.00000 0.000000 X( 6, 7) 0.000000 2.000000 X( 6, 8) 0.000000 0.000000 X( 6, 9) 0.000000 6.000000 X( 6, 10) 0.000000 0.000000 X( 6, 11) 0.000000 1.000000 X( 7, 1) 0.000000 4.000000 X( 7, 2) 0.000000 2.000000 X( 7, 3) 0.000000 5.000000 X( 7, 4) 0.000000 3.000000 X( 7, 5) 0.000000 1.000000 X( 7, 6) 0.000000 2.000000 X( 7, 7) 20.00000 0.000000 X( 7, 8) 0.000000 0.000000 X( 7, 9) 0.000000 99998.00 X( 7, 10) 0.000000 0.000000 X( 7, 11) 0.000000 3.000000 X( 8, 1) 0.000000 5.000000 X( 8, 2) 0.000000 2.000000 X( 8, 3) 0.000000 10.00000 X( 8, 4) 0.000000 4.000000 X( 8, 5) 0.000000 5.000000 X( 8, 6) 0.000000 2.000000 X( 8, 7) 0.000000 2.000000 X( 8, 8) 17.00000 0.000000 X( 8, 9) 3.000000 0.000000 X( 8, 10) 0.000000 3.000000 X( 8, 11) 0.000000 0.000000 X( 9, 1) 0.000000 14.00000 X( 9, 2) 0.000000 11.00000 X( 9, 3) 0.000000 8.000000 X( 9, 4) 0.000000 11.00000 X( 9, 5) 0.000000 7.000000 X( 9, 6) 0.000000 10.00000 X( 9, 7) 0.000000 10002.00 X( 9, 8) 0.000000 2.000000 X( 9, 9) 14.00000 0.000000 X( 9, 10) 0.000000 2.000000 X( 9, 11) 6.000000 0.000000 X( 10, 1) 0.000000 6.000000 X( 10, 2) 0.000000 4.000000 X( 10, 3) 0.000000 14.00000 X( 10, 4) 0.000000 7.000000 X( 10, 5) 0.000000 4.000000 X( 10, 6) 0.000000 4.000000 X( 10, 7) 0.000000 4.000000 X( 10, 8) 0.000000 5.000000 X( 10, 9) 0.000000 2.000000 X( 10, 10) 20.00000 0.000000 X( 10, 11) 0.000000 7.000000 X( 11, 1) 0.000000 14.00000 X( 11, 2) 0.000000 11.00000 X( 11, 3) 0.000000 10.00000 X( 11, 4) 0.000000 10.00000 X( 11, 5) 0.000000 10.00000 X( 11, 6) 0.000000 7.000000 X( 11, 7) 0.000000 9.000000 X( 11, 8) 0.000000 4.000000 X( 11, 9) 0.000000 2.000000 X( 11, 10) 0.000000 9.000000 X( 11, 11) 20.00000 0.000000 Row Slack or Surplus Dual Price OBJ 68.00000 -1.000000 SUP( 1) 0.000000 1.000000 SUP( 2) 0.000000 2.000000 SUP( 3) 0.000000 0.000000 SUP( 4) 0.000000 1.000000 SUP( 5) 0.000000 2.000000 SUP( 6) 0.000000 2.000000 SUP( 7) 0.000000 2.000000 SUP( 8) 0.000000 3.000000 SUP( 9) 0.000000 4.000000 SUP( 10) 0.000000 4.000000 SUP( 11) 0.000000 5.000000 DEM( 1) 0.000000 -1.000000 DEM( 2) 0.000000 -2.000000 DEM( 3) 0.000000 0.000000 DEM( 4) 0.000000 -1.000000 DEM( 5) 0.000000 -2.000000 DEM( 6) 0.000000 -2.000000 DEM( 7) 0.000000 -2.000000 DEM( 8) 0.000000 -3.000000 DEM( 9) 0.000000 -4.000000 DEM( 10) 0.000000 -4.000000 DEM( 11) 0.000000 -5.000000a.model:sets:Warehouse/1..3/:a;Customer/1..4/:b;Routes(Warehouse,Customer):c,x;endsetsdata:a=15,25,5;b=5,15,15,10;c=10,2,20,11,12,7,9,20,2,14,16,18;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 Global optimal solution found. Objective value: 335.0000 Infeasibilities: 0.000000 Total solver iterations: 5 Variable Value Reduced Cost A( 1) 15.00000 0.000000 A( 2) 25.00000 0.000000 A( 3) 5.000000 0.000000 B( 1) 5.000000 0.000000 B( 2) 15.00000 0.000000 B( 3) 15.00000 0.000000 B( 4) 10.00000 0.000000 C( 1, 1) 10.00000 0.000000 C( 1, 2) 2.000000 0.000000 C( 1, 3) 20.00000 0.000000 C( 1, 4) 11.00000 0.000000 C( 2, 1) 12.00000 0.000000 C( 2, 2) 7.000000 0.000000 C( 2, 3) 9.000000 0.000000 C( 2, 4) 20.00000 0.000000 C( 3, 1) 2.000000 0.000000 C( 3, 2) 14.00000 0.000000 C( 3, 3) 16.00000 0.000000 C( 3, 4) 18.00000 0.000000 X( 1, 1) 0.000000 13.00000 X( 1, 2) 5.000000 0.000000 X( 1, 3) 0.000000 16.00000 X( 1, 4) 10.00000 0.000000 X( 2, 1) 0.000000 10.00000 X( 2, 2) 10.00000 0.000000 X( 2, 3) 15.00000 0.000000 X( 2, 4) 0.000000 4.000000 X( 3, 1) 5.000000 0.000000 X( 3, 2) 0.000000 7.000000 X( 3, 3) 0.000000 7.000000 X( 3, 4) 0.000000 2.000000 Row Slack or Surplus Dual Price OBJ 335.0000 -1.000000 SUP( 1) 0.000000 5.000000 SUP( 2) 0.000000 0.000000 SUP( 3) 0.000000 0.000000 DEM( 1) 0.000000 -2.000000 DEM( 2) 0.000000 -7.000000 DEM( 3) 0.000000 -9.000000 DEM( 4) 0.000000 -16.00000b.model:sets:Warehouse/1..3/:a;Customer/1..4/:b;Routes(Warehouse,Customer):c,x;endsetsdata:a=7,25,26;b=10,10,20,15;c=8,4,1,2,6,9,4,7,5,3,4,3;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 Global optimal solution found. Objective value: 193.0000 Infeasibilities: 0.000000 Total solver iterations: 7 Variable Value Reduced Cost A( 1) 7.000000 0.000000 A( 2) 25.00000 0.000000 A( 3) 26.00000 0.000000 B( 1) 10.00000 0.000000 B( 2) 10.00000 0.000000 B( 3) 20.00000 0.000000 B( 4) 15.00000 0.000000 C( 1, 1) 8.000000 0.000000 C( 1, 2) 4.000000 0.000000 C( 1, 3) 1.000000 0.000000 C( 1, 4) 2.000000 0.000000 C( 2, 1) 6.000000 0.000000 C( 2, 2) 9.000000 0.000000 C( 2, 3) 4.000000 0.000000 C( 2, 4) 7.000000 0.000000 C( 3, 1) 5.000000 0.000000 C( 3, 2) 3.000000 0.000000 C( 3, 3) 4.000000 0.000000 C( 3, 4) 3.000000 0.000000 X( 1, 1) 0.000000 5.000000 X( 1, 2) 0.000000 3.000000 X( 1, 3) 7.000000 0.000000 X( 1, 4) 0.000000 1.000000 X( 2, 1) 9.000000 0.000000 X( 2, 2) 0.000000 5.000000 X( 2, 3) 13.00000 0.000000 X( 2, 4) 0.000000 3.000000 X( 3, 1) 1.000000 0.000000 X( 3, 2) 10.00000 0.000000 X( 3, 3) 0.000000 1.000000 X( 3, 4) 15.00000 0.000000 Row Slack or Surplus Dual Price OBJ 193.0000 -1.000000 SUP( 1) 0.000000 3.000000 SUP( 2) 3.000000 0.000000 SUP( 3) 0.000000 1.000000 DEM( 1) 0.000000 -6.000000 DEM( 2) 0.000000 -4.000000 DEM( 3) 0.000000 -4.000000 DEM( 4) 0.000000 -4.000000 喜欢所以热爱,坚持干货分享,欢迎订阅我的微信公众号 呐,请我吃辣条 打赏 微信支付 支付宝