Update app.py

app.py

@@ -137,44 +137,48 @@ def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personalit
         task_word = "order quantity"
         base_info += f"- Shipments In Transit To You (arriving next week onwards): {list(e_state['incoming_shipments'])}"
 
-# --- PERFECT RATIONAL (NORMATIVE) PROMPTS ---
+    # --- PERFECT RATIONAL (NORMATIVE) PROMPTS ---
     
     if llm_personality == 'perfect_rational' and info_sharing == 'full':
         stable_demand = current_stable_demand
         
-        # --- FIX: Use a stable heuristic (Anchor & Adjust) ---
-        # Base Stock Level (target - position) is inherently oscillatory.
-        # Anchor & Adjust is stable.
-        
-        safety_stock = 4 # Target net inventory (ss=0 causes expensive backlogs)
-        anchor_demand = stable_demand
-        inventory_correction = safety_stock - (e_state['inventory'] - e_state['backlog'])
+        # 1. CALCULATE CORRECT LEAD TIME (UNCHANGED)
+        if e_state['name'] == 'Factory': 
+            total_lead_time = FACTORY_LEAD_TIME # 1
+        elif e_state['name'] == 'Distributor': 
+            total_lead_time = ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY # 1+1+1 = 3
+        else: 
+            total_lead_time = ORDER_PASSING_DELAY + SHIPPING_DELAY # 1+2 = 3
+            
+        safety_stock = 4
+        target_inventory_level = (stable_demand * total_lead_time) + safety_stock
         
-        # 2. OSCILLATION FIX: Calculate CORRECT Full Inventory Position
+        # 2. OSCILLATION FIX: Calculate CORRECT Inventory Position
         order_in_transit_to_supplier = st.session_state.game_state['last_week_orders'].get(e_state['name'], 0) # Order Delay (1 week)
         
         if e_state['name'] == 'Factory':
             # Factory pipeline: In Production (1 week)
             supply_line = sum(st.session_state.game_state['factory_production_pipeline'])
-            inv_pos_components = f"(InProd={supply_line})"
+            inventory_position = (e_state['inventory'] - e_state['backlog'] + supply_line)
+            inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InProd={supply_line})"
         
         elif e_state['name'] == 'Distributor':
             # Distributor pipeline: In Shipping (1 week) + In Production (1 week) + Order Delay (1 week)
             in_shipping = sum(e_state['incoming_shipments'])
             in_production = sum(st.session_state.game_state['factory_production_pipeline'])
             supply_line = in_shipping + in_production + order_in_transit_to_supplier
-            inv_pos_components = f"(InTransitShip={in_shipping} + InProd={in_production} + OrderToSupplier={order_in_transit_to_supplier})"
+            inventory_position = (e_state['inventory'] - e_state['backlog'] + supply_line)
+            inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={in_shipping} + InProd={in_production} + OrderToSupplier={order_in_transit_to_supplier})"
         
         else: # Retailer and Wholesaler
             # R/W pipeline: In Shipping (2 weeks) + Order Delay (1 week)
             in_shipping = sum(e_state['incoming_shipments'])
             supply_line = in_shipping + order_in_transit_to_supplier
-            inv_pos_components = f"(InTransitShip={in_shipping} + OrderToSupplier={order_in_transit_to_supplier})"
+            inventory_position = (e_state['inventory'] - e_state['backlog'] + supply_line)
+            inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={in_shipping} + OrderToSupplier={order_in_transit_to_supplier})"
 
-        # This is the new, stable formula
-        optimal_order = max(0, int(anchor_demand + inventory_correction - supply_line))
-        
-        return f"**You are a perfectly rational supply chain AI with full system visibility.**\nYour only goal is to maintain stability and minimize costs based on mathematical optimization.\n**System Analysis (Anchor & Adjust):**\n* **Known Stable End-Customer Demand:** {stable_demand} units/week.\n* **Your Target Net Inventory:** {safety_stock} units.\n* **Your Full Supply Line:** {supply_line} units {inv_pos_components}.\n* **Mathematically Optimal {task_word.title()}:**\n  Order = (Stable Demand) + (Target Inventory - Current Inventory) - (Full Supply Line)\n  Order = {stable_demand} + ({inventory_correction}) - {supply_line} = **{optimal_order} units**.\n**Your Task:** Confirm this optimal {task_word}. Respond with a single integer."
+        optimal_order = max(0, int(target_inventory_level - inventory_position))
+        return f"**You are a perfectly rational supply chain AI with full system visibility.**\nYour only goal is to maintain stability and minimize costs based on mathematical optimization.\n**System Analysis:**\n* **Known Stable End-Customer Demand:** {stable_demand} units/week.\n* **Your Current Total Inventory Position:** {inventory_position} units. {inv_pos_components}\n* **Optimal Target Inventory Level:** {target_inventory_level} units (Target for {total_lead_time} weeks lead time).\n* **Mathematically Optimal {task_word.title()}:** The optimal decision is **{optimal_order} units**.\n**Your Task:** Confirm this optimal {task_word}. Respond with a single integer."
     
     elif llm_personality == 'perfect_rational' and info_sharing == 'local':
         safety_stock = 4