The LP Procedure
- Overview
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Getting Started
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Syntax
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Details
Missing Values Dense Data Input Format Sparse Data Input Format Converting Any PROC LP Format to an MPS-Format SAS Data Set Converting Standard MPS Format to Sparse Format The Reduced Costs, Dual Activities, and Current Tableau Macro Variable ORLP Pricing Scaling Preprocessing Integer Programming Sensitivity Analysis Range Analysis Parametric Programming Interactive Facilities Memory Management Output Data Sets Input Data Sets Displayed Output ODS Table and Variable Names Memory Limit
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Examples
An Oil Blending Problem A Sparse View of the Oil Blending Problem Sensitivity Analysis: Changes in Objective Coefficients Additional Sensitivity Analysis Price Parametric Programming for the Oil Blending Problem Special Ordered Sets and the Oil Blending Problem Goal-Programming a Product Mix Problem A Simple Integer Program An Infeasible Problem Restarting an Integer Program Alternative Search of the Branch-and-Bound Tree An Assignment Problem A Scheduling Problem A Multicommodity Transshipment Problem with Fixed Charges Converting to an MPS-Format SAS Data Set Migration to OPTMODEL: Assignment Migration to OPTMODEL: Multicommodity Transshipment
- References
| An Integer Programming Example |
The following is a simple mixed-integer programming problem. Details can be found in Example 5.8 in the section Examples: LP Procedure.
data;
format _row_ $10.;
input _row_ $ choco gumdr ichoco igumdr _type_ $ _rhs_;
datalines;
object .25 .75 -100 -75 max .
cooking 15 40 0 0 le 27000
color 0 56.25 0 0 le 27000
package 18.75 0 0 0 le 27000
condiments 12 50 0 0 le 27000
chocolate 1 0 -10000 0 le 0
gum 0 1 0 -10000 le 0
only_one 0 0 1 1 eq 1
binary . . 1 2 binary .
;
The row with 'binary' type indicates that this problem is a mixed-integer program and all the integer variables are binary. The integer values of the row set an ordering for PROC LP to pick the branching variable when VARSELECT=PRIOR is chosen. Smaller values will have higher priorities. The _ROW_ variable here is an alias of the _ID_ variable.
This problem can be solved with the following statements:
proc lp canselect=lifo backtrack=obj varselect=far endpause;
run;
quit;
%put &_orlp_;
The options CANSELECT=, BACKTRACK=, and VARSELECT= specify the rules for picking the next active problem and the rule to choose the branching variable. In this example, the values LIFO, OBJ and FAR serve as the default values, so the three options can be omitted from the PROC LP statement. The following is the output from the %PUT statement:
STATUS=SUCCESSFUL PHASE=3 OBJECTIVE=285 P_FEAS=YES D_FEAS=YES INT_ITER=3
INT_FEAS=2 ACTIVE=0 INT_BEST=285 PHASE1_ITER=0 PHASE2_ITER=5
PHASE3_ITER=5
Preprocessing
Using the PREPROCESS= option, you can apply the preprocessing techniques to pre-solve and then solve the preceding mixed-integer program:
proc lp preprocess=1 endpause;
run;
quit;
%put &_orlp_;
The preprocessing statistics are written to the SAS log file as follows:
NOTE: Preprocessing 1 ... NOTE: 2 upper bounds decreased. NOTE: 2 coefficients reduced. NOTE: Preprocessing 2 ... NOTE: 2 constraints eliminated. NOTE: Preprocessing done.
The new output _ORLP_ is as follows:
STATUS=SUCCESSFUL PHASE=3 OBJECTIVE=285 P_FEAS=YES D_FEAS=YES INT_ITER=0
INT_FEAS=1 ACTIVE=0 INT_BEST=285 PHASE1_ITER=0 PHASE2_ITER=5
PHASE3_ITER=0
In this example, the number of integer iterations (INT_ITER=) is zero, which means that the preprocessing has reduced the gap between the relaxed linear problem and the mixed-integer program to zero.