The OPTMODEL Procedure
A Transportation Problem
You can easily translate the symbolic formulation of a problem into the OPTMODEL procedure. Consider the transportation problem, which is mathematically modeled as the following linear programming problem:
where O is the set of origins, D is the set of destinations, is the cost to transport one unit from i to j, is the supply of origin i, is the demand of destination j, and is the decision variable for the amount of shipment from i to j.
Here is a very simple example. The cities in the set O of origins are Detroit and Pittsburgh. The cities in the set D of destinations are Boston and New York. The cost matrix, supply, and demand are shown in Table 10.2.
Table 10.2: A Transportation Problem
Boston | New York | Supply | |
Detroit | 30 | 20 | 200 |
Pittsburgh | 40 | 10 | 100 |
Demand | 150 | 150 |
The problem is compactly and clearly formulated and solved by using the OPTMODEL procedure with the following statements:
proc optmodel;
/* specify parameters */
set O={'Detroit','Pittsburgh'};
set D={'Boston','New York'};
number c{O,D}=[30 20
40 10];
number a{O}=[200 100];
number b{D}=[150 150];
/* model description */
var x{O,D} >= 0;
min total_cost = sum{i in O, j in D}c[i,j]*x[i,j];
constraint supply{i in O}: sum{j in D}x[i,j]=a[i];
constraint demand{j in D}: sum{i in O}x[i,j]=b[j];
/* solve and output */
solve;
print x;
The output is shown in Figure 10.4.
Figure 10.4: Solution to the Transportation Problem
| Problem Summary | |
|---|---|
| Objective Sense | Minimization |
| Objective Function | total_cost |
| Objective Type | Linear |
| Number of Variables | 4 |
| Bounded Above | 0 |
| Bounded Below | 4 |
| Bounded Below and Above | 0 |
| Free | 0 |
| Fixed | 0 |
| Number of Constraints | 4 |
| Linear LE (<=) | 0 |
| Linear EQ (=) | 4 |
| Linear GE (>=) | 0 |
| Linear Range | 0 |
| Constraint Coefficients | 8 |