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:

\[ \begin{array}{lrcllc} \displaystyle \mathop {\textrm{minimize}}& {\displaystyle \mathop \sum _{i\in O, j \in D}}c_{ij}x_{ij}& & & & \\ \textrm{subject to}& {\displaystyle \mathop \sum _{j \in D}x_{ij}} & = & a_ i, & \quad \forall i \in O & (\mr{SUPPLY}) \\ & {\displaystyle \mathop \sum _{i \in O}x_{ij}} & = & b_ j, & \quad \forall j \in D & (\mr{DEMAND}) \\ & x_{ij} & \geq & 0, & \quad \forall (i,j) \in O \times D & \end{array} \]

where O is the set of origins, D is the set of destinations, $c_{ij}$ is the cost to transport one unit from i to j, $a_ i$ is the supply of origin i, $b_ j$ is the demand of destination j, and $x_{ij}$ 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 5.2.

Table 5.2: 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 5.4.

Figure 5.4: Solution to the Transportation Problem

The OPTMODEL Procedure

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

Performance Information
Execution Mode Single-Machine
Number of Threads 1

Solution Summary
Solver LP
Algorithm Dual Simplex
Objective Function total_cost
Solution Status Optimal
Objective Value 6500
   
Primal Infeasibility 0
Dual Infeasibility 0
Bound Infeasibility 0
   
Iterations 0
Presolve Time 0.00
Solution Time 0.00

x
  Boston New York
Detroit 150 50
Pittsburgh 0 100