lumber_comment.sas

proc optmodel; 
   num num_patterns init 0;
   set PATTERNS = 1..num_patterns;
   set cuts; 
   number choices {patterns, cuts}; 
   number prices {patterns}; 
   number lumber {patterns}; 
   number targets {cuts}; 
   number length {cuts}; 
   read data cuts into cuts=[_n_] targets=number length; 

   /* use CLP solver to generate all possible patterns */
   set RAWS;
   num capacity {RAWS};
   num price {RAWS};
   read data lumber into RAWS=[_N_] capacity=length price;
   num r;
   var Y {cuts} >= 0 integer;
   con CapacityCon:
      sum {j in cuts} length[j] * Y[j] <= capacity[r];
   problem CLP_Problem include Y CapacityCon;
   use problem CLP_Problem;
   do r = RAWS;
      put r=;
      solve with CLP / findallsolns;
      for {s in 1.._NSOL_} do;
         num_patterns = num_patterns + 1;
         for {j in cuts} choices[num_patterns,j] = Y[j].sol[s];
         prices[num_patterns] = price[r];
         lumber[num_patterns] = capacity[r];
      end;
   end;
   put num_patterns=;

   /* Selections will contain the patterns in the optimal solution */ 
   var selections {patterns} >= 0 integer; 

   /* Minimizing total cost... */ 
   minimize total_cost = sum{p in patterns} prices[p] * selections[p]; 

   /* Must cut enough of each length! */ 
   con target_con {c in cuts}: 
      sum {p in patterns} selections[p] * choices[p, c] = targets[c]; 

   problem MILP_Problem include selections total_cost target_con;
   use problem MILP_Problem;
   solve; 

   create data outdata(drop=i) from [i] = {i in patterns: selections[i].sol > 0.5}
      chosen = selections[i] {j in cuts} <col('choice'||j)=choices[i,j]> lumber; 
quit;