diff --git a/src/calculation_time_constrains/service.py b/src/calculation_time_constrains/service.py
index cf4d3f9..0e4e69a 100644
--- a/src/calculation_time_constrains/service.py
+++ b/src/calculation_time_constrains/service.py
@@ -17,36 +17,36 @@ from src.overhaul_scope.service import get as get_scope
 from .schema import CalculationResultsRead
 
 
-# def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int,numEquipments:int ,decay_base: float = 1.1) -> np.ndarray:
-#     if overhaul_cost < 0:
-#         raise ValueError("Overhaul cost cannot be negative")
-#     if days <= 0:
-#         raise ValueError("Days must be positive")
-        
-#     exponents = np.arange(0, days)
-#     cost_per_equipment = overhaul_cost / numEquipments
-#     # Using a slower decay base to spread the budget depletion over more days
-#     results = cost_per_equipment / (decay_base ** exponents)
-#     results = np.where(np.isfinite(results), results, 0)
-#     return results
-
-
-def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int, numEquipments: int, decay_base: float = 1.1) -> np.ndarray:
+def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int,numEquipments:int ,decay_base: float = 1.1) -> np.ndarray:
     if overhaul_cost < 0:
         raise ValueError("Overhaul cost cannot be negative")
     if days <= 0:
         raise ValueError("Days must be positive")
-    
+        
     exponents = np.arange(0, days)
     cost_per_equipment = overhaul_cost / numEquipments
-    
-    # Introduce randomness by multiplying with a random factor
-    random_factors = np.random.normal(1.0, 0.1, numEquipments)  # Mean 1.0, Std Dev 0.1
-    results = np.array([cost_per_equipment * factor / (decay_base ** exponents) for factor in random_factors])
-    
+    # Using a slower decay base to spread the budget depletion over more days
+    results = cost_per_equipment / (decay_base ** exponents)
     results = np.where(np.isfinite(results), results, 0)
     return results
 
+
+# def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int, numEquipments: int, decay_base: float = 1.1) -> np.ndarray:
+#     if overhaul_cost < 0:
+#         raise ValueError("Overhaul cost cannot be negative")
+#     if days <= 0:
+#         raise ValueError("Days must be positive")
+    
+#     exponents = np.arange(0, days)
+#     cost_per_equipment = overhaul_cost / numEquipments
+    
+#     # Introduce randomness by multiplying with a random factor
+#     random_factors = np.random.normal(1.0, 0.1, numEquipments)  # Mean 1.0, Std Dev 0.1
+#     results = np.array([cost_per_equipment * factor / (decay_base ** exponents) for factor in random_factors])
+    
+#     results = np.where(np.isfinite(results), results, 0)
+#     return results
+
 # def get_corrective_cost_time_chart(material_cost: float, service_cost: float, days: int) -> Tuple[np.ndarray, np.ndarray]:
 #     day_points = np.arange(0, days)