OK, the other post that I linked to allows fractional Assign values and also has CUSTOMERS = FACILITIES, so that approach does not directly apply.
For your data, I reduced the set of customers (by 3) to avoid infeasibility, as mentioned earlier:
CUSTOMERS = setof {<i,j> in CUSTOMERS_FACILITIES} i;
I used the sparse formulation (as in the "Sparse Modeling" documentation example) with only the Capacitated and SingleSource constraints:
var Assign {CUSTOMERS_FACILITIES} binary;
var Build {FACILITIES} binary;
/* declare implicit variables */
impvar Total_PDStemTravelCost = sum {<i,j> in CUSTOMERS_FACILITIES} PDStemTravelCost[i,j] * Assign[i,j];
impvar Total_FacOperCost = sum {j in FACILITIES} FacOperCost&cap.[j] * Build[j];
/******************************************************
Declare constraints on decision variables with CON
*******************************************************/
/* define constraint: sum of customer demands assigned to a facility must be less than the facility capacity */
con Capacitated {j in FACILITIES}:
sum {<i,(j)> in CUSTOMERS_FACILITIES} CustZipDemand[i] * Assign[i,j] <= FacCapacity&cap.[j] * Build[j];
/* define constraint: each customer is assigned to exactly one facility, all customers are assigned */
con SingleSource {i in CUSTOMERS}:
sum {<(i),j> in CUSTOMERS_FACILITIES} Assign[i,j] = 1;
With the following SOLVE statement, I got a 1% solution after 4.5 hours with SAS/OR 15.1 on my Windows laptop:
solve with milp / relobjgap=0.01 heuristics=aggressive;
Here are the last few lines of the iteration log:
554817 367415 205 1087826 1076877 1.02% 15785
554893 367453 205 1087826 1076877 1.02% 15790
557157 345573 206 1086185 1076877 0.86% 15793
NOTE: Optimal within relative gap.
NOTE: Objective = 1086185.2119.
