The OPTLP Procedure

Example 11.1 Oil Refinery Problem

Consider an oil refinery scenario. A step in refining crude oil into finished oil products involves a distillation process that splits crude into various streams. Suppose there are three types of crude available: Arabian light (a_l), Arabian heavy (a_h), and Brega (br). These crudes are distilled into light naphtha (na_l), intermediate naphtha (na_i), and heating oil (h_o). These in turn are blended into two types of jet fuel. Jet fuel j_1 is made up of 30% intermediate naphtha and 70% heating oil, and jet fuel j_2 is made up of 20% light naphtha and 80% heating oil. What amounts of the three crudes maximize the profit from producing jet fuel (j_1, j_2)? This problem can be formulated as the following linear program:

$\begin{array}{rrcrcrccccc} \max & \multicolumn{9}{l}{ -\, 175\, \mr {a\_ l} - 165\, \mr {a\_ h} - 205\, \mr {br} + 350\, \mr {j\_ 1} + 350\, \mr {j\_ 2}} \\ \mr {subject\ to} & & & & & & & & & \\ (\mr {napha\_ l}) & 0.035\, \mr {a\_ l} & +& 0.03\, \mr {a\_ h} & +& 0.045 \, \mr {br} & =& \mr {na\_ l} & & \\ (\mr {napha\_ i}) & 0.1\, \mr {a\_ l} & +& 0.075\, \mr {a\_ h} & +& 0.135\, \mr {br} & =& \mr {na\_ i} & & \\ (\mr {htg\_ oil}) & 0.39\, \mr {a\_ l} & +& 0.3\, \mr {a\_ h} & +& 0.43\, \mr {br} & =& \mr {h\_ o} & & \\ (\mr {blend1}) & & & 0.3\, \mr {j\_ 1} & & & \leq & \mr {na\_ i} & & \\ (\mr {blend2}) & & & & & 0.2\, \mr {j\_ 2} & \leq & \mr {na\_ l} & & \\ (\mr {blend3}) & & & 0.7\, \mr {j\_ 1} & +& 0.8\, \mr {j\_ 2} & \leq & \mr {h\_ o} & & \\ & \mr {a\_ l} & & & & & \leq & 110 & & \\ & & & \mr {a\_ h} & & & \leq & 165 & & \\ & & & & & \mr {br} & \leq & 80 & & \\ & \multicolumn{5}{r}\mr {a\_ l, a\_ h, br, na\_ 1, na\_ i, h\_ o, j\_ 1, j\_ 2} & \geq & 0 & & \end{array}$

The constraints “blend1” and “blend2” ensure that j_1 and j_2 are made with the specified amounts of na_i and na_l, respectively. The constraint “blend3” is actually the reduced form of the following constraints:

$\begin{array}{ccccc} \textrm{h\_ o1} & & & \geq & 0.7\, \textrm{j\_ 1} \\ & & \textrm{h\_ o2} & \geq & 0.8\, \textrm{j\_ 2} \\ \textrm{h\_ o1} & + & \textrm{h\_ o2} & \leq & \textrm{h\_ o} \end{array}$

where h_o1 and h_o2 are dummy variables.

You can use the following SAS code to create the input data set ex1:

data ex1;
   input field1 $ field2 $ field3 $ field4 field5 $ field6;
   datalines;
NAME        .          EX1      .     .         .
ROWS        .          .        .     .         .
 N          profit     .        .     .         .
 E          napha_l    .        .     .         .
 E          napha_i    .        .     .         . 
 E          htg_oil    .        .     .         .
 L          blend1     .        .     .         .
 L          blend2     .        .     .         .
 L          blend3     .        .     .         .
COLUMNS     .          .        .     .         .
.           a_l        profit   -175  napha_l   .035
.           a_l        napha_i  .100  htg_oil   .390
.           a_h        profit   -165  napha_l   .030
.           a_h        napha_i  .075  htg_oil   .300
.           br         profit   -205  napha_l   .045
.           br         napha_i  .135  htg_oil   .430
.           na_l       napha_l  -1    blend2    -1
.           na_i       napha_i  -1    blend1    -1
.           h_o        htg_oil  -1    blend3    -1
.           j_1        profit   350   blend1    .3
.           j_1        blend3   .7    .         . 
.           j_2        profit   350   blend2    .2
.           j_2        blend3   .8    .         .
BOUNDS      .          .        .     .         .
UP          .          a_l      110   .         .
UP          .          a_h      165   .         .
UP          .          br       80    .         .
ENDATA      .          .        .     .         .
;

You can use the following call to PROC OPTLP to solve the LP problem:

proc optlp data=ex1
   objsense  = max
   algorithm = primal
   primalout = ex1pout
   dualout   = ex1dout
   logfreq   = 1;
run;
%put &_OROPTLP_;

Note that the OBJSENSE=MAX option is used to indicate that the objective function is to be maximized.

The primal and dual solutions are displayed in Output 11.1.1.

Output 11.1.1: Example 1: Primal and Dual Solution Output

Primal Solution

Obs	Objective Function ID	Variable Name	Variable Type	Objective Coefficient	Upper Bound	Variable Value	Variable Status	Reduced Cost
1	profit	a_l	D	-175	110	110.000	U	10.2083
2	profit	a_h	D	-165	165	0.000	L	-22.8125
3	profit	br	D	-205	80	80.000	U	2.8125
4	profit	na_l	N	0	1.7977E308	7.450	B	0.0000
5	profit	na_i	N	0	1.7977E308	21.800	B	0.0000
6	profit	h_o	N	0	1.7977E308	77.300	B	0.0000
7	profit	j_1	N	350	1.7977E308	72.667	B	0.0000
8	profit	j_2	N	350	1.7977E308	33.042	B	0.0000

Dual Solution

Obs	Objective Function ID	Constraint Name	Constraint Type	Constraint Lower Bound	Constraint Upper Bound	Dual Variable Value	Constraint Status	Constraint Activity
1	profit	napha_l	E	.	.	0.000	L	0.00000
2	profit	napha_i	E	.	.	-145.833	U	0.00000
3	profit	htg_oil	E	.	.	-437.500	U	0.00000
4	profit	blend1	L	.	.	145.833	L	-0.00000
5	profit	blend2	L	.	.	0.000	B	-0.84167
6	profit	blend3	L	.	.	437.500	L	-0.00000

The progress of the solution is printed to the log as follows.

Output 11.1.2: Log: Solution Progress

NOTE: The problem EX1 has 8 variables (0 free, 0 fixed).

NOTE: The problem has 6 constraints (3 LE, 3 EQ, 0 GE, 0 range).

NOTE: The problem has 19 constraint coefficients.

WARNING: The objective sense has been changed to maximization.

NOTE: The LP presolver value AUTOMATIC is applied.

NOTE: The LP presolver removed 3 variables and 3 constraints.

NOTE: The LP presolver removed 3 constraint coefficients.

NOTE: The LP presolver formulated the dual of the problem.

NOTE: The presolved problem has 6 variables, 5 constraints, and 16 constraint

coefficients.

NOTE: The LP solver is called.

NOTE: The Primal Simplex algorithm is used.

Objective Entering Leaving

Phase Iteration Value Time Variable Variable

P 1 1 1.400000E+03 0 blend2 j_2 (S)

P 1 2 7.000000E+02 0 blend1 a_l (S)

P 1 3 1.267500E+02 0 br br (S)

P 1 4 1.750000E+01 0 a_l j_1 (S)

P 1 5 0.000000E+00 0

P 2 6 2.820833E+03 0 blend3 blend2

P 2 7 1.347916E+03 0

D 2 8 1.347917E+03 0

NOTE: Optimal.

NOTE: Objective = 1347.9166667.

NOTE: The Primal Simplex solve time is 0.00 seconds.

NOTE: The data set WORK.EX1POUT has 8 observations and 10 variables.

NOTE: The data set WORK.EX1DOUT has 6 observations and 10 variables.

Note that the %put statement immediately after the OPTLP procedure prints value of the macro variable _OROPTLP_ to the log as follows.

Output 11.1.3: Log: Value of the Macro Variable _OROPTLP_

STATUS=OK ALGORITHM=PS SOLUTION_STATUS=OPTIMAL OBJECTIVE=1347.9166667

PRIMAL_INFEASIBILITY=0 DUAL_INFEASIBILITY=0 BOUND_INFEASIBILITY=0

ITERATIONS=8 PRESOLVE_TIME=0.00 SOLUTION_TIME=0.00

The value briefly summarizes the status of the OPTLP procedure upon termination.