Originally posted by: Rym

I have an IPL model,and i want to optimize the number of constraints in a way to combine the  three contraints into only one constraint.

subject to{ 
   cpur:

    forall( j in P)
      sum( i in V) cpui[i]*lumda[i][j] <= cpuj[j];
    

    ramr:
     forall( j in P)
       sum( i in V) rami[i]*lumda[i][j] <= ramj[j];

    diskr: 
     forall( j in P)
       sum( i in V) diski[i]*lumda[i][j] <= diskj[j];
       
       }    

Lumda[i][j] is my decision variable. Any idea???

Thanks

Hi,

what about

range V=1..2;
 
 range P=1..3;
 
 {string} resources={"cpu","ram","disk"};
 
 float usage[v in V][resources]=rand(10);
 
 float capacity[r in resources][p in P]=rand(10);
 
 dvar float+ lumda[V][P];
 
 
 subject to{
 
 forall( j in P)
   forall(r in resources)
      sum( i in V) usage[i][r]*lumda[i][j] <= capacity[r][j];
       
       }    

?

regards