diff --git a/app/services/solver_sandbox.py b/app/services/solver_sandbox.py
index 4f07e5b..05693dc 100644
--- a/app/services/solver_sandbox.py
+++ b/app/services/solver_sandbox.py
@@ -61,27 +61,26 @@ class SolverSandbox:
             break
 
     def yas_elastic(tif_target = 140.0): # 140 is the optimal
-        Items = [1,2,3,4,5]
+        Items   = [1,2,3,4,5]
+        Bundles = [1,2]
 
         # For TIF target
         tif = {
           1: 10,
-          2: 20,
-          3: 40,
-          4: 60,
-          5: 80
+          2: 20
         }
 
         iif = {
           1: 10,
           2: 20,
           3: 30,
-          4: 50,
-          5: 70
+          4: 40,
+          5: 50
         }
         # ---
 
-        items = LpVariable.dicts('Item', Items, cat='Binary')
+        items   = LpVariable.dicts('Item', Items, cat='Binary')
+        bundles = LpVariable.dicts('Bundle', Bundles, cat='Binary')
 
         drift     = 0
         max_drift = 10 # 10% elasticity
@@ -91,20 +90,27 @@ class SolverSandbox:
             problem = LpProblem('TIF_TCC', LpMinimize)
 
             # objective function
-            problem += lpSum([(tif[i] + iif[i]) * items[i] for i in Items])
+            problem += lpSum([items[i] for i in Items])
 
             # Constraint 1
-            problem += lpSum([items[i] for i in Items]) == 3, 'TotalItems'
+            problem += lpSum([items[i] for i in Items] + [bundles[b] for b in Bundles]) == 2, 'TotalItems'
 
             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'
+              [iif[i] * items[i] for i in Items]
+            ) >= tif_target - (tif_target * drift_percent), 'ItemIifMin'
             problem += lpSum(
-              [(tif[i] + iif[i]) * items[i] for i in Items]
-            ) <= tif_target + (tif_target * drift_percent), 'TifIifMax'
+              [iif[i] * items[i] for i in Items]
+            ) <= tif_target + (tif_target * drift_percent), 'ItemIifMax'
+
+            problem += lpSum(
+              [tif[i] * bundles[i] for i in Bundles]
+            ) >= tif_target - (tif_target * drift_percent), 'BundleTifMin'
+            problem += lpSum(
+              [tif[i] * bundles[i] for i in Bundles]
+            ) <= tif_target + (tif_target * drift_percent), 'BundleTifMax'
 
             problem.solve()