1 chapter 2: linear programming problems: basic ideas 2.1 introduction to linear programming 2.2...
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Chapter 2: Linear Programming Problems: Basic Ideas
2.1 Introduction to Linear Programming
2.2 Formulating and Solving Linear Programming Problems Using the OPTMODEL Procedure
2.3 Reading Data from SAS Data Sets
2.4 Writing Output from the OPTMODEL Procedure
2.5 Dual Values, Reduced Costs, and Pricing in the Simplex Method
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2.1 Introduction to Linear Programming2.1 Introduction to Linear Programming
2.2 Formulating and Solving Linear Programming Problems Using the OPTMODEL Procedure
2.3 Reading Data from SAS Data Sets
2.4 Writing Output from the OPTMODEL Procedure
2.5 Dual Values, Reduced Costs, and Pricing in the Simplex Method
Chapter 2: Linear Programming Problems: Basic Ideas
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Objectives Understand how, geometrically, the primal simplex,
dual simplex, and interior point methods solve linear programming problems.
Enter and solve simple linear programming problems using the OPTMODEL procedure.
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A Linear Programming Problem
Each of the linear constraints can be either an inequality or an equation.
The bounds can be ±∞, so that xj can be restricted to be non-negative (lj=0 and uj=+∞) or free (lj=-∞ and uj=+∞).
{ , , }
( )
1 1 n n
j j j
min | max c x +...+c x
subject to
l x u j 1,2,...,n
Ax b
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Implicit Assumptions of Linear Programming Proportionality Additivity Divisibility Certainty
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Two-Dimensional ExampleThe following LP has decision variables x and y:
maximize 12x +19y
subject to x + 3y ≤ 225x + y ≤ 1173x + 4y ≤ 420x ≥ 0, y ≥ 0
The constraints of the LP determine a feasible region in two dimensions.
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Feasible Region
x axis
y ax
is
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Feasible Region
[63, 54]
[0, 75]
[0, 0] [117, 0]
extreme point solutions
optimal solution
y ax
is
x axis
An extreme point is a corner of the feasible region
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This demonstration illustrates the solution of a linear programming problem using PROC OPTMODEL with the default, primal simplex and iterative interior point solvers.
Solving a Linear Programming Problem Using PROC OPTMODEL 2dimensional.sas
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[63, 54]
Primal Simplex Trajectory
[0, 75]
[0, 0] [117, 0]
extreme point solutions
optimal solution
y ax
is
x axis
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[63, 54]
Dual Simplex Trajectory
[0, 75]
[0, 0] [117, 0]
extreme point solutions
optimal solution
y ax
is
x axis
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[63, 54]
Iterative Interior Trajectory
[0, 75]
[0, 0] [117, 0]
extreme point solutions
optimal solution
y ax
is
x axis
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These exercises reinforce the concepts discussed previously.
Exercises 1 and 2
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2.1 Introduction to Linear Programming
2.2 Formulating and Solving Linear Programming 2.2 Formulating and Solving Linear Programming Problems Using the OPTMODEL ProcedureProblems Using the OPTMODEL Procedure
2.3 Reading Data from SAS Data Sets
2.4 Writing Output from the OPTMODEL Procedure
2.5 Dual Values, Reduced Costs, and Pricing in the Simplex Method
Chapter 2: Linear Programming Problems: Basic Ideas
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Objectives Formulate linear programming problems using
array indexing or index sets, name constraints, and store values in arrays and matrices.
Use the EXPAND statement to verify that a formulation is correct.
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A Furniture-Making ProblemA furniture-making company can manufacture desks, chairs, bookcases, and bedframes, all of which require various person-hours of labor and units of metal and wood, given in the table below:
Labor
(hrs)
Metal
(lbs)
Wood
(ft3)
Selling
Price ($)
Desks 2 1 3 52
Chairs 1 1 3 44
Bookcases 3 1 4 70
Bedframes 2 1 4 61
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A Furniture-Making ProblemThe cost and availability of labor, metal, and wood are as follows:
Assuming that all furniture can be sold, how many desks, chairs, bookcases, and bedframes should the company produce per day in order to make its profit as large as possible?
Labor
(hrs)
Metal
(lbs)
Wood
(ft3)
Cost ($) 7 10 5
Availability 225 117 420
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Three Distinct Formulation Approaches
Variable Names explicit enter data intuitive
Array Indices compact read data abstract
Index Sets compact read
data intuitive flexible
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A Furniture-Making ProblemThe cost and availability of labor, metal, and wood are as follows:
Assuming that all furniture can be sold, how many desks, chairs, bookcases, and bedframes should the company produce per day in order to make its profit as large as possible?
What should the decision variables be?
Labor
(hrs)
Metal
(lbs)
Wood
(ft3)
Cost ($) 7 10 5
Availability 225 117 420
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A Furniture-Making ProblemThe cost and availability of labor, metal, and wood are as follows:
Assuming that all furniture can be sold, how many desks, chairs, bookcases, and bedframes should the company produce per day in order to make its profit as large as possible?
What should the decision variables be?
Labor
(hrs)
Metal
(lbs)
Wood
(ft3)
Cost ($) 7 10 5
Availability 225 117 420
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Furniture-Making Problem Data
What should the objective be?
Labor
(hrs)
Metal
(lbs)
Wood
(ft3)
Selling
Price ($)
Desks 2 1 3 52
Chairs 1 1 3 44
Bookcases 3 1 4 70
Bedframes 2 1 4 61
Cost ($) 7 10 5
Availability 225 117 420
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Furniture-Making Problem Data
What should the objective be?
Maximize profit = revenue - cost
Labor
(hrs)
Metal
(lbs)
Wood
(ft3)
Selling
Price ($)
Desks 2 1 3 52
Chairs 1 1 3 44
Bookcases 3 1 4 70
Bedframes 2 1 4 61
Cost ($) 7 10 5
Availability 225 117 420
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Mathematical Optimization FormulationsThe basic structure of the formulation of a mathematical optimization problem is shown here:
min|max objective function
subject to constraints
variable bounds
The formulation should be followed by a description of the decision variables, sets, and parameters in the formulation.
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PROC OPTMODEL FormulationsThe basic structure of a PROC OPTMODEL formulation of a mathematical optimization problem is shown here:
proc optmodel;
/* declare sets and parameters */
/* declare variables */
/* declare constraints */
/* declare objective */
solve;
/* print solution */
quit;
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Formulation Using Variable Names proc optmodel;
/* declare variables */ desks, chairs, bookcases, bedframes
/* declare constraints */ availability of labor, metal, wood
/* declare objective */ maximize profit = revenue - cost
solve;
/* print solution */
quit;
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This demonstration illustrates using the interactive nature of PROC OPTMODEL to expand and then solve a linear programming problem formulated using variable names.
Using PROC OPTMODEL to Solve the Variable Names Formulation of the Furniture-Making Problem furniture_names.sas
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The EXPAND StatementThe EXPAND statement has options to print only a part of the linear programming formulation. This is the statement’s syntax:
Identifier-expression is the name of a variable, objective, or constraint. Options include the following:
VAR outputs variables.
OBJECTIVE|OBJ outputs objectives.
CONSTRAINT|CON outputs constraints.
EXPAND [ identifier-expression ] [ / options ] ;EXPAND [ identifier-expression ] [ / options ] ;
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Arrays versus Names in PROC OPTMODELAn LP is compactly represented by arrays and matrices:
Advantages of arrays and matrices in PROC OPTMODEL: enter objective/constraint coefficients compactly read data from SAS data files make formulations portable/scalable/adaptable
PROC OPTMODEL syntax for matrix and array entries is A[i,j] and b[i].
{ , , }
min | max
subject to
cx
Ax b
l x u
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Example: Transportation ProblemGiven supply values at a set of origins and demand values at a set of destinations, the transportation problem is to determine the amount to transport from each origin to each destination to meet the demand at a minimum cost.
A linear programming model of a transportation problem with two origins and three destinations is
( )
( )
( )
2 3
ij iji=1 j=1
3
ij ij=1
2
ij ji=1
ij
minimize c x
subject to x s i =1,2
x d j =1,2,3
x 0 i =1,2; j =1,2,33
2
11
2Ori
gin
s
De
stination
s
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The SUM Aggregation OperatorThe SUM aggregation operator can be used to add numeric or variable expressions:
PROC OPTMODEL Output
In this sum, k is a local dummy parameter.
print (sum{k in 1..24} k**2);
4900
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The SUM Aggregation OperatorThe SUM aggregation operator can be used to add numeric or variable expressions:
In these sums, i, j, and k are local dummy parameters.
print (sum{k in 1..24} k**2);
min Cost = sum{i in 1..2, j in 1..3} c[i,j] * x[i,j];
con s1: sum{j in 1..3} x[1,j] <= s[1]; con s2: sum{j in 1..3} x[2,j] <= s[2]; con d1: sum{i in 1..2} x[i,1] >= d[1]; con d2: sum{i in 1..2} x[i,2] >= d[2]; con d3: sum{i in 1..2} x[i,3] >= d[3];
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Expanding a Transportation ProblemThe EXPAND statement also expands summations:
( )
( )
( )
2 3
ij iji=1 j=1
3
ij ij=1
2
ij ji=1
ij
minimize c x
subject to x s i =1,2
x d j =1,2,3
x 0 i =1,2; j =1,2,33
2
11
2
Minimize Cost=c11*x[1,1]+c12*x[1,2]+c13*x[1,3]+c21*x[2,1]+ c22*x[2,2]+c23*x[2,3]Constraint s1: x[1,1]+x[1,2]+x[1,3] <= s1
Constraint s2: x[2,1]+x[2,2]+x[2,3] <= s2
Constraint d1: x[1,1]+x[2,1] >= d1
Constraint d2: x[1,2]+x[2,2] >= d2
Constraint d3: x[1,3]+x[2,3] >= d3
Ori
gin
s
De
stination
s
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Declaring Parameters in PROC OPTMODELParameters (other than local dummy parameters) must be declared in PROC OPTMODEL before they are used. number or num declares numeric parameters
num n; num pi = constant('pi'); num M{1..n,1..n};
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Declaring Parameters in PROC OPTMODELParameters (other than local dummy parameters) must be declared in PROC OPTMODEL before they are used. number or num declares numeric parameters:
string or str declares character-valued parameters:
str name; str wkday{1..5}=[Mon Tue Wed Thu Fri];
num n; num pi = constant('pi'); num M{1..n,1..n};
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Declaring Parameters in PROC OPTMODELParameters (other than local dummy parameters) must be declared in PROC OPTMODEL before they are used. number or num declares numeric parameters. string or str declares character-valued parameters. set <number> or set <num> declares a set of
numbers. set <string> or set <str> declares a set of
strings. set <type-1,…,type-n> declares a set of n-tuples
(types can be number (num) or string (str)).
set <num> Sixties = 1960..1969; set <str> Cities = /Cary 'New York'/; set <str,num> Parts = /<R 1> <C 2>/;
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Declaring Parameters in PROC OPTMODELParameters (other than local dummy parameters) must be declared in PROC OPTMODEL before they are used. number or num declares numeric parameters. string or str declares character-valued parameters. set <number> or set <num> declares a set of
numbers. set <string> or set <str> declares a set of
strings. set <type-1,…,type-n> declares a set of n-tuples
(types can be number (num) or string (str)).
set Sixties = 1960..1969; set Cities = /Cary 'New York'/; set Parts = /<R 1> <C 2>/;
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This demonstration illustrates the use of arrays and matrices in PROC OPTMODEL to solve a linear programming problem.
Using Arrays in PROC OPTMODEL to Solve the Furniture-Making Problem furniture_arrays.sas
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Array Indexing versus Index Sets
set Products = /desks chairs bookcases bedframes/; set Resources = /labor metal wood/;
num A{Resources,Products} = [2 1 3 2 1 1 1 1 3 3 4 4]; num s{Products} = [52 44 70 61]; num c{Resources} = [7 10 5]; num b{Resources} = [225 117 420];
The indices used in arrays and matrices often correspond to index sets that are more meaningful than 1,2,…,n:
num A{1..3,1..4} = [2 1 3 2 1 1 1 1 3 3 4 4]; num s{1..4} = [52 44 70 61]; num c{1..3} = [7 10 5]; num b{1..3} = [225 117 420];
Array Indexing
versus Mnemonic Index Sets
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Array Indexing versus Index Sets
set Products = /desks chairs bookcases bedframes/; set Resources = /labor metal wood/;
num Requirements{Resources,Products} = [2 1 3 2 1 1 1 1 3 3 4 4]; num Selling_Price{Products} = [52 44 70 61]; num Cost{Resources} = [7 10 5]; num Availability{Resources} = [225 117 420];
The indices used in arrays and matrices often correspond to index sets that are more meaningful than 1,2,…,n:
num A{1..3,1..4} = [2 1 3 2 1 1 1 1 3 3 4 4]; num s{1..4} = [52 44 70 61]; num c{1..3} = [7 10 5]; num b{1..3} = [225 117 420];
Array Indexing
versus Mnemonic Index Sets (and Names)
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The SUM Aggregation OperatorThe SUM aggregation operator can be used with index sets. This is the statement’s syntax:
If the index-set is a set of tuples, the dummy parameter must match the number of terms in the tuple:
SUM{ index-set } expression SUM{ index-set } expression
set Cities = /Cary 'New York'/; num Total, Population{Cities}; Total = sum{c in Cities} Population[c];
set Parts = /<R 1> <C 2>/; num Inv, Stock{Parts}; Inv = sum{<p,n> in Parts} Stock[p,n];
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This demonstration illustrates the use of index sets in PROC OPTMODEL to solve a linear programming problem.
Using Index Sets in PROC OPTMODEL to Solve the Furniture-Making Problem furniture_indices.sas
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This exercise reinforces the concepts discussed previously.
Exercise 3
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2.1 Introduction to Linear Programming
2.2 Formulating and Solving Linear Programming Problems Using the OPTMODEL Procedure
2.3 Reading Data from SAS Data Sets2.3 Reading Data from SAS Data Sets
2.4 Writing Output from the OPTMODEL Procedure
2.5 Dual Values, Reduced Costs, and Pricing in the Simplex Method
Chapter 2: Linear Programming Problems: Basic Ideas
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Objectives Read data from multiple SAS data sets to formulate
linear programming problems.
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Reading Data from SAS Data Sets: Example
Name Height Weight Age
1 Alfred 69 112.5 14
2 Alice 56.5 84 13
3 Barbara 65.3 98 13
4 Carol 62.8 102.5 14
How can you read the height, weight, and age of students into the arrays Height, Weight, and Age?
SAS Data Set: Opt.Class (just the first four columns)
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Reading Data from SAS Data Sets: Example
Name Height Weight Age
1 Alfred 69 112.5 14
2 Alice 56.5 84 13
3 Barbara 65.3 98 13
4 Carol 62.8 102.5 14
SAS Data Set: Opt.Class (just the first four rows)
read data Opt.Class into Students=[Name] Height Weight Age;
How can you read the height, weight, and age of students into the arrays Height, Weight, and Age?
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Reading Data from SAS Data Sets: Example
Name Height Weight Age
1 Alfred 69 112.5 14
2 Alice 56.5 84 13
3 Barbara 65.3 98 13
4 Carol 62.8 102.5 14
SAS Data Set: Opt.Class (just the first four rows)
Can you read just the first four rows (observations)?
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Reading Data from SAS Data Sets: Example
Name Height Weight Age
1 Alfred 69 112.5 14
2 Alice 56.5 84 13
3 Barbara 65.3 98 13
4 Carol 62.8 102.5 14
SAS Data Set: Opt.Class (just the first four rows)
read data Opt.Class(obs=4) into Students=[Name] Height Weight Age;
Can you read just the first four rows (observations)?
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data Resource_Data; input Resource $ Cost Amount_Available; datalines;labor 7 225metal 10 117wood 5 420run;
data Product_Data; length Product $9; input Product $ Selling_Price labor metal wood; datalines;desks 52 2 1 3chairs 44 1 1 3bookcases 70 3 1 4bedframes 61 2 1 4run;
Furniture-Making Problem SAS Data Sets
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Furniture-Making Problem SAS Data Sets
Resource Cost Amount_Available
1 labor 7 225
2 metal 10 117
3 wood 5 420
Product Selling_Price labor metal wood
1 desks 52 2 1 3
2 chairs 44 1 1 3
3 bookcases 70 3 1 4
4 bedframes 61 2 1 4
SAS Data Set: Work.Resource_Data
SAS Data Set: Work.Product_Data
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Reading Data: The READ Statement
SAS-data-set specifies the input data set. read-key-column(s) provide the index values for array
destinations. The optional set-name saves index values as a set. read-column(s) specify the data values to read and
destination locations. The optional NOMISS keyword suppresses the
assignment of missing values.
READ DATA SAS-data-set [ NOMISS ] INTO [set-name=] [ read-key-column(s) ] [ read-column(s)] ;
READ DATA SAS-data-set [ NOMISS ] INTO [set-name=] [ read-key-column(s) ] [ read-column(s)] ;
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proc optmodel; /* declare sets and parameters */ set <str> Products, Resources; num Cost{Resources}, Availability{Resources}; num Selling_Price{Products}; num Requirements{Resources,Products};
read data Resource_Data into Resources=[Resource] Cost Availability=Amount_Available;
read data Product_Data into Products=[Product] Selling_Price {r in Resources} <Requirements[r,Product]=col(r)>;
Reading the Furniture-Making Data Sets
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Declare index-sets (with no initialization expression, <str> is necessary because the default is <num>).
proc optmodel; /* declare sets and parameters */ set <str> Products, Resources; num Cost{Resources}, Availability{Resources}; num Selling_Price{Products}; num Requirements{Resources,Products};
read data Resource_Data into Resources=[Resource] Cost Availability=Amount_Available;
read data Product_Data into Products=[Product] Selling_Price {r in Resources} <Requirements[r,Product]=col(r)>;
Reading the Furniture-Making Data Sets
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Declare parameter arrays, which are indexed by the (unpopulated) index-sets Products and Resources.
proc optmodel; /* declare sets and parameters */ set <str> Products, Resources; num Cost{Resources}, Availability{Resources}; num Selling_Price{Products}; num Requirements{Resources,Products};
read data Resource_Data into Resources=[Resource] Cost Availability=Amount_Available;
read data Product_Data into Products=[Product] Selling_Price {r in Resources} <Requirements[r,Product]=col(r)>;
Reading the Furniture-Making Data Sets
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data Resource_Data; input Resource $ Cost Amount_Available; datalines;labor 7 225metal 10 117wood 5 420run;
Reading the Furniture-Making Data Sets
read-key-column(SAS data set variable name)
set <str> Products, Resources; num Cost{Resources}, Availability{Resources}; read data Resource_Data into Resources=[Resource] Cost Availability=Amount_Available;
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data Resource_Data; input Resource $ Cost Amount_Available; datalines;labor 7 225metal 10 117wood 5 420run;
set <str> Products, Resources; num Cost{Resources}, Availability{Resources}; read data Resource_Data into Resources=[Resource] Cost Availability=Amount_Available;
Reading the Furniture-Making Data Sets
set-name(OPTMODEL index-set)
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data Resource_Data; input Resource $ Cost Amount_Available; datalines;labor 7 225metal 10 117wood 5 420run;
set <str> Products, Resources; num Cost{Resources}, Availability{Resources}; read data Resource_Data into Resources=[Resource] Cost Availability=Amount_Available;
Reading the Furniture-Making Data Sets
read-columns (OPTMODEL array name [=SAS data set variable name] )
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set <str> Products, Resources; num Selling_Price{Products}; num Requirements{Resources,Products}; read data Product_Data into Products=[Product] Selling_Price {r in Resources} <Requirements[r,Product]=col(r)>;
Reading the Furniture-Making Data Sets
read-key-column (SAS data set variable name)
data Product_Data; length Product $9; input Product $ Selling_Price labor metal wood; datalines;desks 52 2 1 3chairs 44 1 1 3bookcases 70 3 1 4bedframes 61 2 1 4run;
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set <str> Products, Resources; num Selling_Price{Products}; num Requirements{Resources,Products}; read data Product_Data into Products=[Product] Selling_Price {r in Resources} <Requirements[r,Product]=col(r)>;
Reading the Furniture-Making Data Sets
set-name (OPTMODEL index-set)
data Product_Data; length Product $9; input Product $ Selling_Price labor metal wood; datalines;desks 52 2 1 3chairs 44 1 1 3bookcases 70 3 1 4bedframes 61 2 1 4run;
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set <str> Products, Resources; num Selling_Price{Products}; num Requirements{Resources,Products}; read data Product_Data into Products=[Product] Selling_Price {r in Resources} <Requirements[r,Product]=col(r)>;
Reading the Furniture-Making Data Sets
read-column (OPTMODEL array name)
data Product_Data; length Product $9; input Product $ Selling_Price labor metal wood; datalines;desks 52 2 1 3chairs 44 1 1 3bookcases 70 3 1 4bedframes 61 2 1 4run;
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set <str> Products, Resources; num Selling_Price{Products}; num Requirements{Resources,Products}; read data Product_Data into Products=[Product] Selling_Price {r in Resources} <Requirements[r,Product]=col(r)>;
Reading the Furniture-Making Data Sets
iterated read-column (array destination=COL expression)
data Product_Data; length Product $9; input Product $ Selling_Price labor metal wood; datalines;desks 52 2 1 3chairs 44 1 1 3bookcases 70 3 1 4bedframes 61 2 1 4run;
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This demonstration reads data from SAS data sets for the formulation of the furniture-making problem using index sets in PROC OPTMODEL.
Reading Data Sets in PROC OPTMODEL for the Furniture-Making Problem furniture_read.sas
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data Break_Points; input a @@; datalines;0 1 2 5 10 20run;
Reading Data with No Read-Key-ColumnHow can you read break point values into an array a?
read data SAS-DATA-SET into [ SET-NAME=] [READ-KEY-COLUMNS] READ-COLUMN;
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data Break_Points; input a @@; datalines;0 1 2 5 10 20run;
Reading Data with No Read-Key-ColumnHow can you read break point values into an array a?
read data Break_Points into [ SET-NAME=] [READ-KEY-COLUMNS] READ-COLUMN;
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data Break_Points; input a @@; datalines;0 1 2 5 10 20run;
Reading Data with No Read-Key-ColumnHow can you read break point values into an array a?
read data Break_Points into [ SET-NAME=] [READ-KEY-COLUMNS] a;
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data Break_Points; input a @@; datalines;0 1 2 5 10 20run;
Reading Data with No Read-Key-ColumnHow can you read break point values into an array a?
a
1 0
2 1
3 2
4 5
5 10
6 20
SA
S D
ata
Set
: Work.Break_Points
read data Break_Points into [ SET-NAME=] [READ-KEY-COLUMNS] a;
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data Break_Points; input a @@; datalines;0 1 2 5 10 20run;
Reading Data with No Read-Key-ColumnHow can you read break point values into an array a?
_N_ a
1 0
2 1
3 2
4 5
5 10
6 20
read data Break_Points into [ SET-NAME=] [_N_] a;
SA
S D
ata
Set
: Work.Break_Points
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data Break_Points; input a @@; datalines;0 1 2 5 10 20run;
Reading Data with No Read-Key-ColumnHow can you read break point values into an array a?
_N_ a
1 0
2 1
3 2
4 5
5 10
6 20
read data Break_Points into [ SET-NAME=] [_N_] a;
Why would you need the set name?
SA
S D
ata
Set
: Work.Break_Points
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data Break_Points; input a @@; datalines;0 1 2 5 10 20run;
Reading Data with No Read-Key-ColumnHow can you read break point values into an array a?
_N_ a
1 0
2 1
3 2
4 5
5 10
6 20
read data Break_Points into domain= [_N_] a;
Why would you need the set name?
SA
S D
ata
Set
: Work.Break_Points
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Reading Data with Read-Key-Columns
Plant Part_No Init_Inventory
1 Denver WD12X300 340
2 Denver WD18X213 456
3 Boulder WD12X300 120
4 Boulder WD1X1097 1203
SAS Data Set: Colorado.Inventory_by_Plant
read data SAS-DATA-SET into [ SET-NAME=] [READ-KEY-COLUMNS] READ-COLUMN;
How can you read the starting inventory levels into the matrix Start[p,n] indexed by Plant and Part_No?
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Reading Data with Read-Key-Columns
Plant Part_No Init_Inventory
1 Denver WD12X300 340
2 Denver WD18X213 456
3 Boulder WD12X300 120
4 Boulder WD1X1097 1203
SAS Data Set: Colorado.Inventory_by_Plant
read data Colorado.Inventory_by_Plant into [READ-KEY-COLUMNS] READ-COLUMN;
How can you read the starting inventory levels into the matrix Start[p,n] indexed by Plant and Part_No?
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Reading Data with Read-Key-Columns
Plant Part_No Init_Inventory
1 Denver WD12X300 340
2 Denver WD18X213 456
3 Boulder WD12X300 120
4 Boulder WD1X1097 1203
SAS Data Set: Colorado.Inventory_by_Plant
read data Colorado.Inventory_by_Plant into [Plant Part_No] READ-COLUMN;
How can you read the starting inventory levels into the matrix Start[p,n] indexed by Plant and Part_No?
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Reading Data with Read-Key-Columns
Plant Part_No Init_Inventory
1 Denver WD12X300 340
2 Denver WD18X213 456
3 Boulder WD12X300 120
4 Boulder WD1X1097 1203
SAS Data Set: Colorado.Inventory_by_Plant
read data Colorado.Inventory_by_Plant into [Plant Part_No] Start=Init_Inventory;
How can you read the starting inventory levels into the matrix Start[p,n] indexed by Plant and Part_No?
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Example: Oil Refinery Blending ProblemHow much of each raw gasoline should be blended into each aviation gasoline to maximize profit while meeting limits on performance (PN) and vapor pressure (RVP)?
Raw Gasolines
B
ACat
Str
Iso
Alk
Aviation Gasolines
PN ≥ 100RVP ≤ 7
PN ≥ 91RVP ≤ 7
PN RVP
107 5
93 8
87 4
108 21
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This class participation exercise includes the use of index sets and reading data from SAS data sets in PROC OPTMODEL to solve a linear programming problem.
Reading Data Sets in PROC OPTMODEL for the Oil Refinery Blending Problemblending_partial.sas
82
These exercises reinforce the concepts discussed previously.
Exercises 4–6
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2.1 Introduction to Linear Programming
2.2 Formulating and Solving Linear Programming Problems Using the OPTMODEL Procedure
2.3 Reading Data from SAS Data Sets
2.4 Writing Output from the OPTMODEL 2.4 Writing Output from the OPTMODEL Procedure Procedure
2.5 Dual Values, Reduced Costs, and Pricing in the Simplex Method
Chapter 2: Linear Programming Problems: Basic Ideas
84
Objectives Interpret PROC OPTMODEL output and write
formatted output. Write linear programming problems to Math
Programming System (MPS) format.
85
PROC OPTMODEL Output: Suffixes Decision Variables.init (initial value) .lb (lower bound).ub (upper bound).sol (solution value) .rc or .dual (reduced cost)
Objective Function
.sol (objective value) Constraints.body (body value) .lb (lower bound).ub (upper bound) .dual (dual value)
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Writing Output: The CREATE StatementThe CREATE statement mimics the READ statement:
SAS-data-set specifies the output data set. key-column(s) specify data set variable names whose
values index array locations in column(s). The optional key-set specifies a set of index values
for key-column(s). column(s) specify data set variable names and
PROC OPTMODEL source data.
CREATE DATA SAS-data-set FROM [ key-column(s) ] [ = key-set ] column(s) ;
CREATE DATA SAS-data-set FROM [ key-column(s) ] [ = key-set ] column(s) ;
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Writing Output from the Furniture-Making LP
Product Solution_Value
1 desks 0
2 chairs 48
3 bookcases 39
4 bedframes 30
SAS Data Set: Work.Optimal_Solution
create data Optimal_Solution from [Product]=Products Solution_Value=x;
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Writing Output from the Furniture-Making LP
Product Solution_Value
1 desks 0
2 chairs 48
3 bookcases 39
4 bedframes 30
SAS Data Set: Work.Optimal_Solution
create data Optimal_Solution from [Product]=Products Solution_Value=x;
Reminder:
Products is the set of products that can be made (an index-set).
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Writing Output from the Furniture-Making LP
Product Solution_Value
1 desks 0
2 chairs 48
3 bookcases 39
4 bedframes 30
SAS Data Set: Work.Optimal_Solution
create data Optimal_Solution from [Product]=Products Solution_Value=x;
Reminder:
x=x.sol is an array that holds the optimal production levels.
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Writing Output from the Furniture-Making LP
Product Solution_Value
1 desks 0
2 chairs 48
3 bookcases 39
4 bedframes 30
SAS Data Set: Work.Optimal_Solution
The key-column declares index values and their data set variables.
create data Optimal_Solution from [Product]=Products Solution_Value=x;
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Writing Output from the Furniture-Making LP
Product Solution_Value
1 desks 0
2 chairs 48
3 bookcases 39
4 bedframes 30
SAS Data Set: Work.Optimal_Solution
The key-set specifies a set of index values for the key-column(s).
create data Optimal_Solution from [Product]=Products Solution_Value=x;
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Writing Output from the Furniture-Making LP
Product Solution_Value
1 desks 0
2 chairs 48
3 bookcases 39
4 bedframes 30
SAS Data Set: Work.Optimal_Solution
The column specifies data set variables and OPTMODEL source data.
create data Optimal_Solution from [Product]=Products Solution_Value=x;
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Writing Output from the Furniture-Making LPHow could you write just the variables that occur (are nonzero) in the optimal solution?
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Writing Output from the Furniture-Making LPHow could you write just the variables that occur (are nonzero) in the optimal solution? Two approaches:
Data set option where=
Optimal_Solution(where=(Solution_Value>0))
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Writing Output from the Furniture-Making LPHow could you write just the variables that occur (are nonzero) in the optimal solution? Two approaches:
Data set option where=
Restrict the key-set by a selection expression
Optimal_Solution(where=(Solution_Value>0))
[Product]={p in Products: x[p]>0}
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Writing Output with Multiple Key ColumnsHow can we write only those transportation links that occur in the optimal solution to a SAS data set Shipping_Links?
create data Shipping_Links from [Origin Destination]={i in Origins, j in Destinations: x[i,j]>0} Amount=x;
create data Shipping_Links(where=(Amount>0)) [Origin Destination] Amount=x;
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Writing Output Using Dummy ParametersDummy parameters in the key-set can also be used to specify the columns:
create data Excess(where=(Amount>0)) from [Supplier]={i in Origins} Amount=(Supply[i]-Supply_Avail[i].body);
SAS Data Set: Work.ExcessSupplier Amount
1 Atlanta 175
2 Los_Angeles 150
3 Bozeman 25
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Writing Output Using Dummy ParametersDummy parameters in the key-set can also be used to specify the columns:
create data Excess(where=(Amount>0)) from [Supplier]={i in Origins} Amount=(Supply[i]-Supply_Avail[i].body);
SAS Data Set: Work.ExcessSupplier Amount
1 Atlanta 175
2 Los_Angeles 150
3 Bozeman 25
Amount does not need to be declared.
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Formatted Output Using the PRINT Statement print 'Profit:' (Profit.sol) DOLLAR.;
Profit: $1,827
PROC OPTMODEL Output
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Formatted Output Using the PRINT Statement print 'Profit:' (Profit.sol) DOLLAR.; print 'Optimal Solution:'; print {p in Products: x[p]>0} x;
Profit: $1,827
Optimal Solution:
[1] x
bedframes 30 bookcases 39 chairs 48
PROC OPTMODEL Output
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Formatted Output Using the PRINT Statement print 'Resource Usage:'; print Usage.body Usage.ub;
Resource Usage:
Usage. [1] BODY Usage.UB
labor 225 225 metal 117 117 wood 420 420
PROC OPTMODEL Output
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Formatted Output Using the PUT StatementThe FILE statement selects the current output file for the PUT statement (the default is the SAS log).
file print; put / 'Products=' Products; put 'Resources=' Resources; file 'greeting.txt'; put 'hello, world'; closefile 'greeting.txt';
(The slash outputs the current line.)
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Formatted Output Using the PUT StatementThe FILE statement selects the current output file for the PUT statement (the default is the SAS log).
PROC OPTMODEL Output
file print; put / 'Products=' Products; put 'Resources=' Resources; file 'greeting.txt'; put 'hello, world'; closefile 'greeting.txt';
Products={'desks','chairs','bookcases','bedframes'} Resources={'labor','metal','wood'}
(The slash outputs the current line.)
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This demonstration illustrates the different methods of writing output from PROC OPTMODEL after solving a linear programming problem.
Writing PROC OPTMODEL Output for the Furniture-Making Problem furniture_output.sas
106
Sparsity in Linear Programming ProblemsMany practical linear programming problems are sparse: most coefficients in the constraint matrix A are zero.
An LP with Staircase Structure
388 rows
466 columns
1,534 nonzeros
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Writing Problems to MPS FormatThe SAVE MPS statement saves the structure and coefficients for an LP model into a SAS data set, which can be used as input data for the OPTLP procedure.
A Supply Chain Problem
108,500 rows
1.7 million columns
4.2 million nonzeros
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This demonstration illustrates writing a linear programming model to the (industry standard) MPS format, which preserves sparsity.
Writing an LP to MPS Format Using PROC OPTMODEL transportation.sas
109
These exercises reinforce the concepts discussed previously.
Exercises 7–9
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2.1 Introduction to Linear Programming
2.2 Formulating and Solving Linear Programming Problems Using the OPTMODEL Procedure
2.3 Reading Data from SAS Data Sets
2.4 Writing Output from the OPTMODEL Procedure
2.5 Dual Values, Reduced Costs, and Pricing in 2.5 Dual Values, Reduced Costs, and Pricing in the Simplex Methodthe Simplex Method
Chapter 2: Linear Programming Problems: Basic Ideas
111
Objectives Explain the interpretation of dual values in linear
programming. Describe how dual values are used in the primal
simplex method and how pricing options influence the behavior of the simplex method.
112
Interpretation of Dual Values The dual value of a constraint is defined as
Dual Value = Change in optimal objective
Unit increase in constraint RHS
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Interpretation of Dual Values The dual value of a constraint is defined as
This assumes that the extreme point determining the optimal solution is not over-determined:
Dual Value = Change in optimal objective
Unit increase in constraint RHS
114
Interpretation of Dual Values The dual value of a constraint is defined as
This assumes that the extreme point determining the optimal solution is not over-determined:
Dual Value = Change in optimal objective
Unit increase in constraint RHS
over-determined (degenerate)
115
[63, 54]
Feasible Region
y ax
is
x axis
optimal solution
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[63, 54]
Feasible Region
dual value = 3.5
dual value = 8.5
dual value = 0
y ax
is
x axis
optimal solution
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Feasible Region
y ax
is
x axis
optimal solution
[60.5, 56.5]
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Feasible Region
y ax
is
x axis
optimal solution
[58, 59]
119
Feasible Region
y ax
is
x axis
optimal solution
[55.5, 61.5]
120
Feasible Region
y ax
is
x axis
optimal solution
[70.5, 51.5]
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Feasible Region
y ax
is
x axis
optimal solution
[72, 51]
122
Dual Values for the Furniture-Making ProblemThe dual values can be written using the PRINT statement:
print Usage.dual;
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Dual Values for the Furniture-Making ProblemThe dual values can be written using the PRINT statement:
Usage. [1] DUAL
labor 2 metal 1 wood 3
PROC OPTMODEL Output print Usage.dual;
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Dual Values for the Furniture-Making ProblemThe dual values can be written using the PRINT statement:
If additional overtime hours are available for $10.50 (time-and-a-half), would they be used?
PROC OPTMODEL Output print Usage.dual;
Usage. [1] DUAL
labor 2 metal 1 wood 3
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Dual Values for the Furniture-Making ProblemThe dual values can be written using the PRINT statement:
If additional overtime hours are available for $10.50 (time-and-a-half), would they be used?
No. For an additional hour at $7, the objective increases by at most $2; for overtime the cost is $10.50=$7+$3.50.
PROC OPTMODEL Output print Usage.dual;
Usage. [1] DUAL
labor 2 metal 1 wood 3
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Making Tables: Pricing an ActivityTables require 3 hours of labor, 1 pound of metal, and 2 ft3 wood. Tables sell for $55. Should any be produced?
Profit for a table:
$55-3($7)-1($10)-2($5) = $14
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Making Tables: Pricing an ActivityTables require 3 hours of labor, 1 pound of metal, and 2 ft3 wood. Tables sell for $55. Should any be produced?
Profit for a table:
$55-3($7)-1($10)-2($5) = $14
Cost of reduced availability:
3($2)+1($1)+2($3) = $13
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Making Tables: Pricing an ActivityTables require 3 hours of labor, 1 pound of metal, and 2 ft3 wood. Tables sell for $55. Should any be produced?
$14-$13=$1 is the unit gain of making tables.
Profit for a table:
$55-3($7)-1($10)-2($5) = $14
Cost of reduced availability:
3($2)+1($1)+2($3) = $13
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Making Tables: Pricing an ActivityTables require 3 hours of labor, 1 pound of metal, and 2 ft3 wood. Tables sell for $55. Should any be produced?
$14-$13=$1 is the unit gain of making tables.
Profit for a table:
$55-3($7)-1($10)-2($5) = $14
Cost of reduced availability:
3($2)+1($1)+2($3) = $13
The difference is called the reduced cost.
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Pricing in the Primal and Dual Simplex Methods
Option Description
FULL Most negative* reduced cost
PARTIAL Maintain a candidate queue (of length QUEUESIZE=k)
STEEPESTEDGE Steepest edge pricing strategy (dual simplex default)
DEVEX Approximate steepest edge
HYBRID Hybrid of DEVEX and STEEPESTEDGE (primal simplex default)
SOLVE WITH LP / PRICETYPE = option ; SOLVE WITH LP / PRICETYPE = option ;
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This demonstration introduces the linear programming problem on which the following exercise is based.
Solving the McDonald’s Diet Problem Using PROC OPTMODEL mcdonalds.sas
132
These exercises reinforce the concepts discussed previously.
Exercises 10–12