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
| Dense Data Input Format |
In the dense format, a model is expressed in a similar way as it is formulated. Each SAS variable corresponds to a model’s column and each SAS observation corresponds to a model’s row. A SAS variable in the input data set is one of the following:
a type variable
an id variable
a structural variable
a right-hand-side variable
a right-hand-side sensitivity analysis variable
a range variable
The type variable tells PROC LP how to interpret the observation as a part of the mathematical programming problem. It identifies and classifies objectives, constraints, and the rows that contain information of variables like types, bounds, and so on. PROC LP recognizes the following keywords as values for the type variable: MIN, MAX, EQ, LE, GE, SOSEQ, SOSLE, UNRSTRT, LOWERBD, UPPERBD, FIXED, INTEGER, BINARY, BASIC, PRICESEN, and FREE. The values of the id variable are the names of the rows in the model. The other variables identify and classify the columns with numerical values.
The TYPE, ID (or ROW), and RHS statements can be omitted if the input data set contains variables _TYPE_, _ID_ (or _ROW_), and _RHS_; otherwise, they must be used. The VAR statement is optional. When it is not specified, PROC LP uses as structural variables all numeric variables not explicitly or implicitly included in statement lists. The RHSSEN and RANGE statements are optional statements for sensitivity and range analyses. They can be omitted if the input data set contains the _RHSSEN_ and _RANGE_ variables.