Hi. I have an optimization problem where we are selecting students from different states with the highest scores for a national competition. The objective is to select the best students aka maximize the score. The constraints are: 1. At least 50% of the students have to be female (weighted by score, not count) 2. The top state concentration <= 30% (weighted by score, not count) 3. All other concentration <= 15% (weighted by score, not count) I am pretty new to optmodel and I am struggling with constraints 2 &3. For #2, I am not sure how to identify the top state out of the list that will be selected by the solver and how to then put a upper bound to it. For #3, I could put the states in individual columns in the students dataset and assign them a 0 or 1, like I did for the sex column. I could then add a family of constraints like this, but I'm not sure if that's the best way of going about it. State1[s] * score[s] * inclusion[s] <=.15 * score[s] * inclusion[s] /*sex 0 = male, 1 = female*/ dataset students;
length name $32. state $2.;
input name $ state $ score sex;
datalines;
Ally TX 90 1
Sam VA 50 0
Pamela TX 76 1
Arnold WA 99 0 Nathan TX 85 0
;
dataset allStates;
length state $2.;
input state $;
datalines;
AL
AK
AZ
.
.
.
WY
; proc optmodel; set <string> studentSet; str stuName{studentSet}; str state{studentSet}; num score{studentSet}; num sex{studentSet}; var Inclusion{studentSet} binary; read data students into studentSet = [stuName = name] state score sex; maximize totalScore = sum{s in studentSet} score[s] * Inclusion[s]; con generCon: sum{s in studentSet} gender[s] * score[s] * Inclusion[s] >=.5 * sum{s in studentSet} score[s] * Inclusion[s]; solve; print Inclusion; quit;
... View more
