I have the below optmodel code to solve an LP problem with optmodel. This is a very large model and the program runs 2.5 hours to solve. My question is, this model spends 2 hours searching for negative solutions and the actual answer is never negative. Is there a way to tell optmodel that the cost must be => 0 and to skip the negative tries?
minimize cost =
sum{<k,f,p,t> in ixPS} XP[k,f,p,t] * XPCost[p]
+ sum{<k,f,p,t> in ixPU} PU[k,f,p,t] * PUCost[k,f,p,t]
+ sum{<ki,ko,w,pl,p,t> in ixPK} PK[ki,ko,w,pl,p,t] * pkgCost[ko,p,w]
+ sum{<k,f,fic,p,t> in ixIK} IK[k,f,fic,p,t] * invCost[k,p]
+ sum{<f,p,t> in ixIKMax1} X5[f,p,t] * X5Cst
+ sum{<fic,p,t> in ixIKMax2} X6[fic,p,t] * X6Cst
+ sum{<k,t> in ixMaxSku} XA[k,t] * XACst
+ sum{<k,t> in ixMinSku} XI[k,t] * XICst
+ sum{<k,f,fic,p,t> in ixIR} IR[k,f,fic,p,t] * LRInvCost[p,t]
+ sum{<k,f,fic,m,p1,p2,t> in ixTS} TS[k,f,fic,m,p1,p2,t] * TScost[k,f,fic,m,p1,p2,t]
+ sum{<k,f,fic,c,p,m,t> in ixS1} S1[k,f,fic,c,p,m,t] * FrtCost[k,f,fic,c,p,m,t]
+ sum{<k,f,fic,c,p,m,t> in ixS2} S2[k,f,fic,c,p,m,t] * OuCost[k,f,fic,c,p,m,t]
+ sum{<k,c,m,t> in ixDMD1} X1[k,c,m,t] * X1Cst
+ sum{<k,c,p,m,t> in ixMD1} XMD1[k,c,p,m,t] * X1Cst
+ sum{<k,c,m,t> in ixDMD2} X2[k,c,m,t] * X2Cst
+ sum{<k,c,p,m,t> in ixMD2} XMD2[k,c,p,m,t] * X2Cst
+ sum{<k,f,m,p,pr,t> in ixLR} LR[k,f,m,p,pr,t] * LRCost[k,f,m,p,pr,t]
+ sum{<c,p,t> in ixCP3Coeff} X3[c,p,t] * X3Cst
+ sum{<f,p,t> in ixMinBulk} X4[f,p,t] * X4Cst
+ sum{<k,g,t> in ixSkuAvgDem} X1Mos[k,g,t] * X1MosCst
+ sum{<k,g,t> in ixSkuAvgDem} X2Mos[k,g,t] * X2MosCst
+ sum{<p,k,t> in ixFFTP} FFDX[p,k,t] * XFCst
+ sum{<p,w,k,k2,t> in ixWCSal} X7[p,w,k,k2,t] * X7Cst
+ sum{<p,w,k,k2,t> in ixWCSal} X8[p,w,k,k2,t] * X8Cst
;
Thanks,
Roger
