sbmack
commented
2 years ago Is this because of how MILPModel handles multidimensional decision variables? I could probably form my decision variables x to be along a single vector if necessary.
Right. MILPModel was formulated with significant performance improvement over the original MIPModel. But using arrays in MILPModel constraints requires the use of the colwise auxiliary function which users found not intuitive. Dirk has since updated MIPModel incorporating the performance improvements of MILPModel while maintaining the formulation elements of the original MIPModel.
So it is my impression now that the updated MIPModel supercedes MILPModel. So your MIPModel version is what you should use.
datadrivensupplychain
I am building a
MILPModelwhere the primary decision variables are binary,x[i,j]. Depending upon thei,jpair, I may want to forcex[i,j]to 0 instead of giving the model the choice of 0 or 1. I have a static matrix,allowed_binary, that isirows andjcolumns, with value of 0 ifx[i,j]must be 0, and a value of 1 ifx[i,j]can be 0 or 1. I made a very simple constraint:add_constraint(x[i,j] <= allowed_binary[i,j], i=1:10, j = 1:20)This constraint is accepted by
omprwith aMIPModelbut when I add it in aMILPModel, I get the errorError in constraint$lhs - constraint$rhs : non-numeric argument to binary operator. Is this because of howMILPModelhandles multidimensional decision variables? I could probably form my decision variablesxto be along a single vector if necessary.