The INTPOINT Procedure

Missing S Supply and Missing D Demand Values

In some models, you may want a node to be either a supply or demand node but you want the node to supply or demand the optimal number of flow units. To indicate that a node is such a supply node, use a missing S value in the SUPPLY list variable in the ARCDATA= data set or the SUPDEM list variable in the NODEDATA= data set. To indicate that a node is such a demand node, use a missing D value in the DEMAND list variable in the ARCDATA= data set or the SUPDEM list variable in the NODEDATA= data set.

Suppose the oil example in the section Introductory NPSC Example is changed so that crude oil can be obtained from either the Middle East or U.S.A. in any amounts. You should specify that the node middle east is a supply node, but you do not want to stipulate that it supplies 100 units, as before. The node u.s.a. should also remain a supply node, but you do not want to stipulate that it supplies 80 units. You must specify that these nodes have missing S supply capabilities:

title  'Oil Industry Example';
title3 'Crude Oil can come from anywhere';
data miss_s;
   missing S;
   input   _node_&$15. _sd_;
   datalines;
middle east         S
u.s.a.              S
servstn1 gas      -95
servstn1 diesel   -30
servstn2 gas      -40
servstn2 diesel   -15
;

The following PROC INTPOINT run uses the same ARCDATA= and CONDATA= data sets used in the section Introductory NPSC Example:

proc intpoint
   bytes=100000
   nodedata=miss_s       /* the supply (missing S) and */
                         /* demand data                */
   arcdata=arcd1         /* the arc descriptions       */
   condata=cond1         /* the side constraints       */
   conout=solution;      /* the solution data set      */
   run;


proc print;
   var _from_ _to_ _cost_ _capac_ _lo_ _flow_ _fcost_;
   sum _fcost_;
   run;

The following messages appear on the SAS log:

NOTE: Number of nodes= 14 .

NOTE: All supply nodes have unspecified (.S) supply capability. Number of these

nodes= 2 .

NOTE: Number of demand nodes= 4 .

NOTE: Total supply= 0 , total demand= 180 .

NOTE: Number of arcs= 18 .

NOTE: Number of <= side constraints= 0 .

NOTE: Number of == side constraints= 2 .

NOTE: Number of >= side constraints= 2 .

NOTE: Number of side constraint coefficients= 8 .

NOTE: The following messages relate to the equivalent Linear Programming

problem solved by the Interior Point algorithm.

NOTE: Number of <= constraints= 0 .

NOTE: Number of == constraints= 17 .

NOTE: Number of >= constraints= 2 .

NOTE: Number of constraint coefficients= 48 .

NOTE: Number of variables= 20 .

NOTE: After preprocessing, number of <= constraints= 0.

NOTE: After preprocessing, number of == constraints= 5.

NOTE: After preprocessing, number of >= constraints= 2.

NOTE: The preprocessor eliminated 12 constraints from the problem.

NOTE: The preprocessor eliminated 33 constraint coefficients from the problem.

NOTE: After preprocessing, number of variables= 6.

NOTE: The preprocessor eliminated 14 variables from the problem.

NOTE: 6 columns, 0 rows and 6 coefficients were added to the problem to handle

unrestricted variables, variables that are split, and constraint slack or

surplus variables.

NOTE: There are 19 sub-diagonal nonzeroes in the unfactored A Atranspose matrix.

NOTE: The 7 factor nodes make up 2 supernodes

NOTE: There are 4 nonzero sub-rows or sub-columns outside the supernodal

triangular regions along the factors leading diagonal.

NOTE: Bound feasibility attained by iteration 1.

NOTE: Dual feasibility attained by iteration 1.

NOTE: Constraint feasibility attained by iteration 1.

NOTE: The Primal-Dual Predictor-Corrector Interior Point algorithm performed 6

iterations.

NOTE: Optimum reached.

NOTE: Objective= 50075.

NOTE: The data set WORK.SOLUTION has 18 observations and 10 variables.

NOTE: There were 18 observations read from the data set WORK.ARCD1.

NOTE: There were 6 observations read from the data set WORK.MISS_S.

NOTE: There were 4 observations read from the data set WORK.COND1.

The CONOUT= data set is shown in Figure 4.11.

Figure 4.11: Missing S SUPDEM Values in NODEDATA

Obs	_from_	_to_	_cost_	_capac_	_lo_	_FLOW_	_FCOST_
1	refinery 1	r1	200	175	50	145.000	29000.00
2	refinery 2	r2	220	100	35	35.000	7700.00
3	r1	ref1 diesel	0	75	0	36.250	0.00
4	r1	ref1 gas	0	140	0	108.750	0.00
5	r2	ref2 diesel	0	75	0	8.750	0.00
6	r2	ref2 gas	0	100	0	26.250	0.00
7	middle east	refinery 1	63	95	20	20.000	1260.00
8	u.s.a.	refinery 1	55	99999999	0	125.000	6875.00
9	middle east	refinery 2	81	80	10	10.000	810.00
10	u.s.a.	refinery 2	49	99999999	0	25.000	1225.00
11	ref1 diesel	servstn1 diesel	18	99999999	0	30.000	540.00
12	ref2 diesel	servstn1 diesel	36	99999999	0	0.000	0.00
13	ref1 gas	servstn1 gas	15	70	0	68.750	1031.25
14	ref2 gas	servstn1 gas	17	35	5	26.250	446.25
15	ref1 diesel	servstn2 diesel	17	99999999	0	6.250	106.25
16	ref2 diesel	servstn2 diesel	23	99999999	0	8.750	201.25
17	ref1 gas	servstn2 gas	22	60	0	40.000	880.00
18	ref2 gas	servstn2 gas	31	99999999	0	0.000	0.00
							50075.00

The optimal supplies of nodes middle east and u.s.a. are 30 and 150 units, respectively. For this example, the same optimal solution is obtained if these nodes had supplies less than these values (each supplies 1 unit, for example) and the THRUNET option was specified in the PROC INTPOINT statement. With the THRUNET option active, when total supply exceeds total demand, the specified nonmissing demand values are the lowest number of flow units that must be absorbed by the corresponding node. This is demonstrated in the following PROC INTPOINT run. The missing S is most useful when nodes are to supply optimal numbers of flow units and it turns out that for some nodes, the optimal supply is 0.

data miss_s_x;
   missing S;
   input   _node_&$15. _sd_;
   datalines;
middle east         1
u.s.a.              1
servstn1 gas      -95
servstn1 diesel   -30
servstn2 gas      -40
servstn2 diesel   -15
;

proc intpoint
   bytes=100000
   thrunet
   nodedata=miss_s_x     /* No supply (missing S)      */
   arcdata=arcd1         /* the arc descriptions       */
   condata=cond1         /* the side constraints       */
   conout=solution;      /* the solution data set      */
   run;

proc print;
   var _from_ _to_ _cost_ _capac_ _lo_ _flow_ _fcost_;
   sum _fcost_;
   run;

The following messages appear on the SAS log. Note that the Total supply= 2, not 0 as in the last run:

NOTE: Number of nodes= 14 .

NOTE: Number of supply nodes= 2 .

NOTE: Number of demand nodes= 4 .

NOTE: Total supply= 2 , total demand= 180 .

NOTE: Number of arcs= 18 .

NOTE: Number of <= side constraints= 0 .

NOTE: Number of == side constraints= 2 .

NOTE: Number of >= side constraints= 2 .

NOTE: Number of side constraint coefficients= 8 .

NOTE: The following messages relate to the equivalent Linear Programming problem

solved by the Interior Point algorithm.

NOTE: Number of <= constraints= 0 .

NOTE: Number of == constraints= 17 .

NOTE: Number of >= constraints= 2 .

NOTE: Number of constraint coefficients= 48 .

NOTE: Number of variables= 20 .

NOTE: After preprocessing, number of <= constraints= 0.

NOTE: After preprocessing, number of == constraints= 5.

NOTE: After preprocessing, number of >= constraints= 2.

NOTE: The preprocessor eliminated 12 constraints from the problem.

NOTE: The preprocessor eliminated 33 constraint coefficients from the problem.

NOTE: After preprocessing, number of variables= 6.

NOTE: The preprocessor eliminated 14 variables from the problem.

NOTE: 6 columns, 0 rows and 6 coefficients were added to the problem to handle

unrestricted variables, variables that are split, and constraint slack or

surplus variables.

NOTE: There are 19 sub-diagonal nonzeroes in the unfactored A Atranspose matrix.

NOTE: The 7 factor nodes make up 2 supernodes

NOTE: There are 4 nonzero sub-rows or sub-columns outside the supernodal triangular

regions along the factors leading diagonal.

NOTE: Bound feasibility attained by iteration 1.

NOTE: Dual feasibility attained by iteration 1.

NOTE: Constraint feasibility attained by iteration 1.

NOTE: The Primal-Dual Predictor-Corrector Interior Point algorithm performed 6

iterations.

NOTE: Optimum reached.

NOTE: Objective= 50075.

NOTE: The data set WORK.SOLUTION has 18 observations and 10 variables.

NOTE: There were 18 observations read from the data set WORK.ARCD1.

NOTE: There were 6 observations read from the data set WORK.MISS_S_X.

NOTE: There were 4 observations read from the data set WORK.COND1.

If total supply exceeds total demand, any missing S values are ignored. If total demand exceeds total supply, any missing D values are ignored.