diff --git a/app/services/solver_sandbox.py b/app/services/solver_sandbox.py
index d7852d5..c44442b 100644
--- a/app/services/solver_sandbox.py
+++ b/app/services/solver_sandbox.py
@@ -61,7 +61,7 @@ class SolverSandbox:
               print(v.name, "=", v.varValue)
             break
 
-    def yas_elastic(tif_target = 50.0):
+    def yas_elastic(tif_target = 140.0): # 140 is the optimal
         Items = [1,2,3,4,5]
 
         # For TIF target
@@ -82,57 +82,55 @@ class SolverSandbox:
         }
         # ---
 
-        total_forms = 2
-
         items = LpVariable.dicts('Item', Items, cat='Binary')
 
-        for form in range(total_forms):
-            drift     = 0
-            max_drift = 10 # 10% elasticity
+        drift     = 0
+        max_drift = 10 # 10% elasticity
 
-            while drift <= max_drift:
-                drift_percent = drift / 100
-                problem = LpProblem('TIF_TCC', LpMinimize)
+        while drift <= max_drift:
+            drift_percent = drift / 100
+            problem = LpProblem('TIF_TCC', LpMinimize)
 
-                # objective function
-                problem += lpSum([(tif[i] + iif[i]) * items[i] for i in Items])
+            # objective function
+            problem += lpSum([(tif[i] + iif[i]) * items[i] for i in Items])
 
-                # Constraint 1
-                problem += lpSum([items[i] for i in Items]) == 3, 'TotalItems'
+            # Constraint 1
+            problem += lpSum([items[i] for i in Items]) == 3, 'TotalItems'
 
-                print(f"Calculating TIF target of {tif_target} with drift of {drift} for Form {form + 1}")
+            print(f"Calculating TIF target of {tif_target} with drift of {drift}%")
 
-                # Our own "Elastic Constraints"
-                problem += lpSum(
-                  [(tif[i] + iif[i]) * items[i] for i in Items]
-                ) >= tif_target - (tif_target * drift_percent), 'TifIifMin'
-                problem += lpSum(
-                  [(tif[i] + iif[i]) * items[i] for i in Items]
-                ) <= tif_target + (tif_target * drift_percent), 'TifIifMax'
+            # Our own "Elastic Constraints"
+            problem += lpSum(
+              [(tif[i] + iif[i]) * items[i] for i in Items]
+            ) >= tif_target - (tif_target * drift_percent), 'TifIifMin'
+            problem += lpSum(
+              [(tif[i] + iif[i]) * items[i] for i in Items]
+            ) <= tif_target + (tif_target * drift_percent), 'TifIifMax'
 
-                problem.solve()
+            problem.solve()
 
-                if LpStatus[problem.status] == 'Infeasible':
-                    print(f"attempt infeasible for drift of {drift}")
+            if LpStatus[problem.status] == 'Infeasible':
+                print(f"attempt infeasible for drift of {drift}")
 
-                    for v in problem.variables():
-                        print(v.name, "=", v.varValue)
+                for v in problem.variables():
+                    print(v.name, "=", v.varValue)
 
-                    print(problem.constraints)
-                    print(problem.objective)
+                print(problem.objective.value())
+                print(problem.constraints)
+                print(problem.objective)
 
-                    drift += 1
+                drift += 1
 
-                else:
-                    print(f"solution found with drift of {drift}!")
+            else:
+                print(f"solution found with drift of {drift}!")
 
-                    for v in problem.variables():
-                        print(v.name, "=", v.varValue)
+                for v in problem.variables():
+                    print(v.name, "=", v.varValue)
 
-                    print(problem.constraints)
-                    print(problem.objective)
+                print(problem.constraints)
+                print(problem.objective)
 
-                    break
+                break
 
     # Implementation of the Whiskas Cat problem, with elastic constraints
     # https://www.coin-or.org/PuLP/CaseStudies/a_blending_problem.html