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Update app.py
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app.py
CHANGED
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@@ -1,5 +1,5 @@
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# app.py
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# @title Beer Game Final Version (
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# -----------------------------------------------------------------------------
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# 1. Import Libraries
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@@ -35,7 +35,8 @@ INITIAL_BACKLOG = 0
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ORDER_PASSING_DELAY = 1 # Handled by last_week_orders
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SHIPPING_DELAY = 2 # General shipping delay (R->W, W->D)
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FACTORY_LEAD_TIME = 1
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HOLDING_COST = 0.5
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BACKLOG_COST = 1.0
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@@ -86,9 +87,9 @@ def init_game_state(llm_personality: str, info_sharing: str, participant_id: str
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for i, name in enumerate(roles):
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upstream = roles[i + 1] if i + 1 < len(roles) else None
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downstream = roles[i - 1] if i - 1 >= 0 else None
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if name == "Distributor": shipping_weeks = FACTORY_SHIPPING_DELAY
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elif name == "Factory": shipping_weeks = 0
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else: shipping_weeks = SHIPPING_DELAY
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st.session_state.game_state['echelons'][name] = {
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'name': name, 'inventory': INITIAL_INVENTORY, 'backlog': INITIAL_BACKLOG,
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'incoming_shipments': deque([0] * shipping_weeks, maxlen=shipping_weeks),
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@@ -121,21 +122,31 @@ def get_llm_order_decision(prompt: str, echelon_name: str) -> (int, str):
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st.error(f"API call failed for {echelon_name}: {e}. Defaulting to 4.")
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return 4, f"API_ERROR: {e}"
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def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personality: str, info_sharing: str, all_echelons_state_decision_point: dict) -> str:
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# This function's logic
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e_state = echelon_state_decision_point
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base_info = f"Your Current Status at the **{e_state['name']}** for **Week {week}** (Before Shipping):\n- On-hand inventory: {e_state['inventory']} units.\n- Backlog (total unfilled orders): {e_state['backlog']} units.\n- Incoming order this week (just received): {e_state['incoming_order']} units.\n"
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if e_state['name'] == 'Factory':
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task_word = "production quantity"
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base_info += f"- Your Production Pipeline (completing next week onwards): {list(st.session_state.game_state['factory_production_pipeline'])}"
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else:
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task_word = "order quantity"
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base_info += f"- Shipments In Transit To You (arriving next week onwards): {list(e_state['incoming_shipments'])}"
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if llm_personality == 'perfect_rational' and info_sharing == 'full':
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stable_demand =
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safety_stock = 4
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target_inventory_level = (stable_demand * total_lead_time) + safety_stock
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if e_state['name'] == 'Factory':
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@@ -147,6 +158,7 @@ def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personalit
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inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={sum(e_state['incoming_shipments'])} + OrderToSupplier={order_in_transit_to_supplier})"
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optimal_order = max(0, int(target_inventory_level - inventory_position))
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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."
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elif llm_personality == 'perfect_rational' and info_sharing == 'local':
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safety_stock = 4; anchor_demand = e_state['incoming_order']
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inventory_correction = safety_stock - (e_state['inventory'] - e_state['backlog'])
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calculated_order = anchor_demand + inventory_correction - supply_line
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rational_local_order = max(0, int(calculated_order))
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return f"**You are a perfectly rational supply chain AI with ONLY LOCAL information.**\nYou must use a logical heuristic to make a stable decision. A proven method is \"Anchoring and Adjustment\".\n\n{base_info}\n\n**Rational Calculation (Anchoring & Adjustment):**\n1. **Anchor on Demand:** Your best guess for future demand is your last incoming order: **{anchor_demand} units**.\n2. **Adjust for Inventory:** You want to hold a safety stock of {safety_stock} units. Your current stock (before shipping) is {e_state['inventory'] - e_state['backlog']}. You need to order an extra **{inventory_correction} units** to correct this.\n3. **Account for {supply_line_desc}:** You already have **{supply_line} units** being processed. These should be subtracted from your new decision.\n\n**Final Calculation:**\n* Decision = (Anchor Demand) + (Inventory Adjustment) - ({supply_line_desc})\n* Decision = {anchor_demand} + {inventory_correction} - {supply_line} = **{rational_local_order} units**.\n**Your Task:** Confirm this locally rational {task_word}. Respond with a single integer."
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elif llm_personality == 'human_like' and info_sharing == 'full':
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full_info_str = f"\n**Full Supply Chain Information (State Before Shipping):**\n- End-Customer Demand this week: {get_customer_demand(week)} units.\n"
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for name, other_e_state in all_echelons_state_decision_point.items():
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if name != e_state['name']: full_info_str += f"- {name}: Inv={other_e_state['inventory']}, Backlog={other_e_state['backlog']}\n"
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return f"""
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**You are a supply chain manager ({e_state['name']}) with full system visibility.**
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You can see everyone's current inventory and backlog before shipping, and the real customer demand.
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{base_info}
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{full_info_str}
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**Your Task:** Your primary responsibility is to meet the demand from your direct customer (your `Incoming order this week`: **{e_state['incoming_order']}** units), which contributes to your total current backlog of {e_state['backlog']}.
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While you can see the stable end-customer demand ({get_customer_demand(week)} units), your priority is to fulfill the order you just received and manage your inventory/backlog.
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You are still human and might get anxious about your own stock levels.
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What {task_word} should you decide on this week? Respond with a single integer.
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"""
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elif llm_personality == 'human_like' and info_sharing == 'local':
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return f"""
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**You are a reactive supply chain manager for the {e_state['name']}.** You have a limited view and tend to over-correct based on fear.
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Your top priority is to NOT have a backlog.
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{base_info}
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**Your Task:** You just received an incoming order for **{e_state['incoming_order']}** units, adding to your total backlog.
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Your gut instinct is to panic and {task_word.split(' ')[0]} enough to ensure you are never caught with a backlog again, considering your current inventory.
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**React emotionally.** What is your knee-jerk {task_word}? Respond with a single integer.
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"""
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def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: int):
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# This
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state = st.session_state.game_state
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week, echelons, human_role = state['week'], state['echelons'], state['human_role']
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llm_personality, info_sharing = state['llm_personality'], state['info_sharing']
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opening_inventories = {name: e['inventory'] for name, e in echelons.items()}
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opening_backlogs = {name: e['backlog'] for name, e in echelons.items()}
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# --- LOG FIX (
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opening_arriving_next_week_UI_VALUE = {name: 0 for name in echelon_order}
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# Factory
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factory_q = state['factory_production_pipeline']
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if factory_q:
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# R, W, D
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for name in ["Retailer", "Wholesaler", "Distributor"]:
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shipment_q = echelons[name]['incoming_shipments']
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if shipment_q:
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#
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if name == 'Distributor':
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# "Next" for Distributor is what's
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if factory_q:
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elif name in ("Retailer", "Wholesaler"):
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# --- END OF LOG FIX (v3) ---
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inventory_after_arrival = {}
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factory_state = echelons["Factory"]
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produced_units = 0
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if state['factory_production_pipeline']:
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produced_units = state['factory_production_pipeline'].popleft()
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inventory_after_arrival["Factory"] = factory_state['inventory'] + produced_units
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for name in ["Retailer", "Wholesaler", "Distributor"]:
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arrived_shipment = 0
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if echelons[name]['incoming_shipments']:
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arrived_shipment = echelons[name]['incoming_shipments'].popleft()
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inventory_after_arrival[name] = echelons[name]['inventory'] + arrived_shipment
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total_backlog_before_shipping = {}
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for name in echelon_order:
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incoming_order_for_this_week = 0
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decision_point_states[name] = {
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'name': name, 'inventory': inventory_after_arrival[name],
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'backlog': total_backlog_before_shipping[name], 'incoming_order': echelons[name]['incoming_order'],
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'incoming_shipments': echelons[name]['incoming_shipments'].copy() if name != "Factory" else deque()
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}
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current_week_orders = {}
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for name in echelon_order:
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for key in ['inventory', 'backlog', 'incoming_order', 'order_placed', 'shipment_sent', 'weekly_cost', 'total_cost']: log_entry[f'{name}.{key}'] = e[key]
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log_entry[f'{name}.llm_raw_response'] = llm_raw_responses.get(name, "")
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# --- LOG FIX (
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log_entry[f'{name}.opening_inventory'] = opening_inventories[name]
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log_entry[f'{name}.opening_backlog'] = opening_backlogs[name]
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log_entry[f'{name}.arrived_this_week'] =
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if name != 'Factory':
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log_entry[f'{name}.arriving_next_week'] =
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else:
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log_entry[f'{name}.production_completing_next_week'] =
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# --- END OF LOG FIX (
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log_entry[f'{human_role}.initial_order'] = human_initial_order; log_entry[f'{human_role}.ai_suggestion'] = ai_suggestion
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state['logs'].append(log_entry)
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state['week'] += 1; state['decision_step'] = 'initial_order'; state['last_week_orders'] = current_week_orders
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state['current_ai_suggestion'] = None # Clean up
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if state['week'] > WEEKS: state['game_running'] = False
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def plot_results(df: pd.DataFrame, title: str, human_role: str):
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# This function remains correct.
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st.markdown("---")
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st.subheader("Supply Chain Status (Start of Week State)")
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# =============== MODIFIED UI LOGIC (
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if info_sharing == 'full':
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cols = st.columns(4)
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for i, name in enumerate(echelon_order):
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st.metric("Inventory (Opening)", e['inventory'])
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st.metric("Backlog (Opening)", e['backlog'])
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# 移除成本显示
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# --- NEW: Added Arriving This Week ---
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current_incoming_order = 0
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if name == "Retailer":
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current_incoming_order = get_customer_demand(week)
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st.write(f"Incoming Order (This Week): **{current_incoming_order}**")
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if name == "Factory":
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# FIX: 'Arriving This Week' (Completing This Week) removed from UI
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# FIX: 'Next week' for Factory is the order it just received from Distributor
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prod_completing_next = state['last_week_orders'].get("Distributor", 0)
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st.write(f"Completing Next Week: **{prod_completing_next}**")
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else:
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# FIX: 'Arriving This Week' removed from UI
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arriving_next = 0
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#
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if name == 'Distributor':
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#
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arriving_next = list(
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elif len(e['incoming_shipments']) == 1 and name in ('Wholesaler', 'Retailer'):
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# R/W: q_len=1, 'next' is [0] (as 'this' is 0)
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arriving_next = list(e['incoming_shipments'])[0]
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# (if q_len=0, arriving_next remains 0)
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# (if q_len=1 and is Distributor, arriving_next remains 0, which is correct)
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st.write(f"Arriving Next Week: **{arriving_next}**")
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st.write(f"**Incoming Order (This Week):**\n# {current_incoming_order}")
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with col3:
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#
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#
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# Arriving NEXT week (Distributor's local view CANNOT see factory pipeline)
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arriving_next = 0
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st.write(f"**Shipment Arriving (Next Week):**\n# {arriving_next}")
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# =======================================================
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st.header("Your Decision (Step 4)")
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# Prepare the state snapshot for the AI prompt (State AFTER arrivals/orders, BEFORE shipping)
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# This logic remains correct and is NOT a UI element
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all_decision_point_states = {}
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for name in echelon_order:
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e_curr = echelons[name] # This is END OF LAST WEEK state
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inv_after_arrival = e_curr['inventory'] + arrived
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backlog_after_new_order = e_curr['backlog'] + inc_order_this_week
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all_decision_point_states[name] = {
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'name': name, 'inventory': inv_after_arrival, 'backlog': backlog_after_new_order,
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'incoming_order': inc_order_this_week,
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# app.py
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# @title Beer Game Final Version (v9 - Correct LT=3, Correct UI, Correct Log)
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# -----------------------------------------------------------------------------
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# 1. Import Libraries
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ORDER_PASSING_DELAY = 1 # Handled by last_week_orders
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SHIPPING_DELAY = 2 # General shipping delay (R->W, W->D)
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FACTORY_LEAD_TIME = 1
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# This is CORRECT for LT=3 (1 pass + 1 produce + 1 ship = 3 week total LT)
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FACTORY_SHIPPING_DELAY = 1
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HOLDING_COST = 0.5
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BACKLOG_COST = 1.0
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for i, name in enumerate(roles):
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upstream = roles[i + 1] if i + 1 < len(roles) else None
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downstream = roles[i - 1] if i - 1 >= 0 else None
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if name == "Distributor": shipping_weeks = FACTORY_SHIPPING_DELAY # This is 1
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elif name == "Factory": shipping_weeks = 0
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else: shipping_weeks = SHIPPING_DELAY # This is 2
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st.session_state.game_state['echelons'][name] = {
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'name': name, 'inventory': INITIAL_INVENTORY, 'backlog': INITIAL_BACKLOG,
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'incoming_shipments': deque([0] * shipping_weeks, maxlen=shipping_weeks),
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st.error(f"API call failed for {echelon_name}: {e}. Defaulting to 4.")
|
| 123 |
return 4, f"API_ERROR: {e}"
|
| 124 |
|
| 125 |
+
# =============== PROMPT FUNCTION (v3 - Sterman Heuristic + Demand Fix) ===============
|
| 126 |
def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personality: str, info_sharing: str, all_echelons_state_decision_point: dict) -> str:
|
| 127 |
+
# This function's logic is updated for "human_like" to follow a flawed Sterman heuristic.
|
| 128 |
e_state = echelon_state_decision_point
|
| 129 |
base_info = f"Your Current Status at the **{e_state['name']}** for **Week {week}** (Before Shipping):\n- On-hand inventory: {e_state['inventory']} units.\n- Backlog (total unfilled orders): {e_state['backlog']} units.\n- Incoming order this week (just received): {e_state['incoming_order']} units.\n"
|
| 130 |
+
|
| 131 |
+
# --- PROMPT FIX: Get correct demand (current, not future) ---
|
| 132 |
+
current_stable_demand = get_customer_demand(week) # Use current week's demand
|
| 133 |
+
|
| 134 |
if e_state['name'] == 'Factory':
|
| 135 |
task_word = "production quantity"
|
| 136 |
base_info += f"- Your Production Pipeline (completing next week onwards): {list(st.session_state.game_state['factory_production_pipeline'])}"
|
| 137 |
else:
|
| 138 |
task_word = "order quantity"
|
| 139 |
base_info += f"- Shipments In Transit To You (arriving next week onwards): {list(e_state['incoming_shipments'])}"
|
| 140 |
+
|
| 141 |
+
# --- PERFECT RATIONAL (NORMATIVE) PROMPTS ---
|
| 142 |
+
|
| 143 |
if llm_personality == 'perfect_rational' and info_sharing == 'full':
|
| 144 |
+
stable_demand = current_stable_demand # Use the correct demand
|
| 145 |
+
# --------------------- LT=3 (1+1+1) ---------------------
|
| 146 |
+
if e_state['name'] == 'Factory': total_lead_time = FACTORY_LEAD_TIME # 1
|
| 147 |
+
elif e_state['name'] == 'Distributor': total_lead_time = ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY # 1+1+1 = 3
|
| 148 |
+
else: total_lead_time = ORDER_PASSING_DELAY + SHIPPING_DELAY # 1+2 = 3
|
| 149 |
+
# ----------------------------------------------------
|
| 150 |
safety_stock = 4
|
| 151 |
target_inventory_level = (stable_demand * total_lead_time) + safety_stock
|
| 152 |
if e_state['name'] == 'Factory':
|
|
|
|
| 158 |
inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={sum(e_state['incoming_shipments'])} + OrderToSupplier={order_in_transit_to_supplier})"
|
| 159 |
optimal_order = max(0, int(target_inventory_level - inventory_position))
|
| 160 |
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."
|
| 161 |
+
|
| 162 |
elif llm_personality == 'perfect_rational' and info_sharing == 'local':
|
| 163 |
safety_stock = 4; anchor_demand = e_state['incoming_order']
|
| 164 |
inventory_correction = safety_stock - (e_state['inventory'] - e_state['backlog'])
|
|
|
|
| 172 |
calculated_order = anchor_demand + inventory_correction - supply_line
|
| 173 |
rational_local_order = max(0, int(calculated_order))
|
| 174 |
return f"**You are a perfectly rational supply chain AI with ONLY LOCAL information.**\nYou must use a logical heuristic to make a stable decision. A proven method is \"Anchoring and Adjustment\".\n\n{base_info}\n\n**Rational Calculation (Anchoring & Adjustment):**\n1. **Anchor on Demand:** Your best guess for future demand is your last incoming order: **{anchor_demand} units**.\n2. **Adjust for Inventory:** You want to hold a safety stock of {safety_stock} units. Your current stock (before shipping) is {e_state['inventory'] - e_state['backlog']}. You need to order an extra **{inventory_correction} units** to correct this.\n3. **Account for {supply_line_desc}:** You already have **{supply_line} units** being processed. These should be subtracted from your new decision.\n\n**Final Calculation:**\n* Decision = (Anchor Demand) + (Inventory Adjustment) - ({supply_line_desc})\n* Decision = {anchor_demand} + {inventory_correction} - {supply_line} = **{rational_local_order} units**.\n**Your Task:** Confirm this locally rational {task_word}. Respond with a single integer."
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
| 175 |
|
| 176 |
+
# --- HUMAN-LIKE (DESCRIPTIVE) PROMPTS ---
|
| 177 |
+
# (These are NEW and implement a flawed, panicky Sterman-style heuristic)
|
| 178 |
+
|
| 179 |
+
else: # Catches both 'human_like' / 'local' and 'human_like' / 'full'
|
| 180 |
+
|
| 181 |
+
# This is the flawed Sterman heuristic
|
| 182 |
+
DESIRED_INVENTORY = 12 # Matches initial inventory
|
| 183 |
+
anchor_demand = e_state['incoming_order']
|
| 184 |
+
net_inventory = e_state['inventory'] - e_state['backlog']
|
| 185 |
+
stock_correction = DESIRED_INVENTORY - net_inventory
|
| 186 |
+
panicky_order = max(0, int(anchor_demand + stock_correction))
|
| 187 |
+
panicky_order_calc = f"{anchor_demand} (Your Incoming Order) + {stock_correction} (Your Stock Correction)"
|
| 188 |
+
|
| 189 |
+
# Get supply line info *just to show* the AI it's being ignored
|
| 190 |
+
if e_state['name'] == 'Factory':
|
| 191 |
+
supply_line = sum(st.session_state.game_state['factory_production_pipeline'])
|
| 192 |
+
supply_line_desc = "In Production"
|
| 193 |
+
else:
|
| 194 |
+
order_in_transit_to_supplier = st.session_state.game_state['last_week_orders'].get(e_state['name'], 0)
|
| 195 |
+
supply_line = sum(e_state['incoming_shipments']) + order_in_transit_to_supplier
|
| 196 |
+
supply_line_desc = "Supply Line"
|
| 197 |
+
|
| 198 |
+
if info_sharing == 'local':
|
| 199 |
+
return f"""
|
| 200 |
+
**You are a reactive supply chain manager for the {e_state['name']}.** You have a limited (local) view.
|
| 201 |
+
You tend to make **reactive, 'gut-instinct' decisions** (like the classic Sterman 1989 model) that cause the Bullwhip Effect.
|
| 202 |
+
|
| 203 |
+
{base_info}
|
| 204 |
+
|
| 205 |
+
**Your Flawed 'Human' Heuristic:**
|
| 206 |
+
Your gut tells you to fix your entire inventory problem *right now*, and you're afraid of your backlog.
|
| 207 |
+
A 'rational' player would account for their {supply_line_desc} (which is {supply_line} units), but you're too busy panicking to trust that.
|
| 208 |
+
|
| 209 |
+
**Your 'Panic' Calculation (Ignoring the Supply Line):**
|
| 210 |
+
1. **Anchor on Demand:** You just got an order for **{anchor_demand}** units. You'll order *at least* that.
|
| 211 |
+
2. **Correct for Stock:** Your desired 'safe' inventory is {DESIRED_INVENTORY}. Your current net inventory is {net_inventory}. You need to order **{stock_correction}** more units to feel safe again.
|
| 212 |
+
3. **Ignore Supply Line:** You'll ignore the **{supply_line} units** already in your pipeline.
|
| 213 |
+
|
| 214 |
+
**Final Panic Order:** (Your Incoming Order) + (Your Stock Correction)
|
| 215 |
+
* Order = {panicky_order_calc} = **{panicky_order} units**.
|
| 216 |
+
|
| 217 |
+
**Your Task:** Confirm this 'gut-instinct' {task_word}. Respond with a single integer.
|
| 218 |
+
"""
|
| 219 |
+
|
| 220 |
+
elif info_sharing == 'full':
|
| 221 |
+
# Build the "Full Info" string just for context
|
| 222 |
+
full_info_str = f"\n**Full Supply Chain Information (State Before Shipping):**\n- End-Customer Demand this week: {current_stable_demand} units.\n"
|
| 223 |
+
for name, other_e_state in all_echelons_state_decision_point.items():
|
| 224 |
+
if name != e_state['name']: full_info_str += f"- {name}: Inv={other_e_state['inventory']}, Backlog={other_e_state['backlog']}\n"
|
| 225 |
+
|
| 226 |
+
return f"""
|
| 227 |
+
**You are a supply chain manager ({e_state['name']}) with full system visibility.**
|
| 228 |
+
{base_info}
|
| 229 |
+
{full_info_str}
|
| 230 |
+
|
| 231 |
+
**A "Human-like" Flawed Decision:**
|
| 232 |
+
Even though you have full information, you are judged by *your own* performance (your inventory, your backlog).
|
| 233 |
+
You tend to react to your *local* situation (like the classic Sterman 1989 model) instead of using the complex full-system data.
|
| 234 |
+
A 'rational' player would use the end-customer demand ({current_stable_demand}) and account for the *entire* system, but your gut-instinct is to panic about *your* numbers.
|
| 235 |
+
|
| 236 |
+
**Your 'Panic' Calculation (Ignoring Full Info and Your Supply Line):**
|
| 237 |
+
1. **Anchor on *Your* Demand:** You just got an order for **{anchor_demand}** units. You react to this, not the end-customer demand.
|
| 238 |
+
2. **Correct for *Your* Stock:** Your desired 'safe' inventory is {DESIRED_INVENTORY}. Your current net inventory is {net_inventory}. You need to order **{stock_correction}** more units.
|
| 239 |
+
3. **Ignore *Your* Supply Line:** You'll ignore the **{supply_line} units** in your own pipeline ({supply_line_desc}).
|
| 240 |
+
|
| 241 |
+
**Final Panic Order:** (Your Incoming Order) + (Your Stock Correction)
|
| 242 |
+
* Order = {panicky_order_calc} = **{panicky_order} units**.
|
| 243 |
+
|
| 244 |
+
**Your Task:** Confirm this 'gut-instinct', locally-focused {task_word}. Respond with a single integer.
|
| 245 |
+
"""
|
| 246 |
+
# =========================================================
|
| 247 |
+
|
| 248 |
+
# =============== STEP_GAME (v9 - Stable Logic + Correct Log Fix) ===============
|
| 249 |
def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: int):
|
| 250 |
+
# This version uses the stable v2 game logic AND the correct v9 logging fix
|
| 251 |
state = st.session_state.game_state
|
| 252 |
week, echelons, human_role = state['week'], state['echelons'], state['human_role']
|
| 253 |
llm_personality, info_sharing = state['llm_personality'], state['info_sharing']
|
|
|
|
| 258 |
opening_inventories = {name: e['inventory'] for name, e in echelons.items()}
|
| 259 |
opening_backlogs = {name: e['backlog'] for name, e in echelons.items()}
|
| 260 |
|
| 261 |
+
# --- LOG FIX (v9): Capture UI-visible values BEFORE any pops ---
|
| 262 |
+
arrived_this_week_LOG = {name: 0 for name in echelon_order}
|
| 263 |
+
arriving_next_week_LOG = {name: 0 for name in echelon_order}
|
|
|
|
|
|
|
|
|
|
| 264 |
factory_q = state['factory_production_pipeline']
|
| 265 |
+
|
| 266 |
+
# Factory UI Values
|
| 267 |
if factory_q:
|
| 268 |
+
arrived_this_week_LOG["Factory"] = factory_q[0] # Arrives this week
|
| 269 |
+
arriving_next_week_LOG["Factory"] = state['last_week_orders'].get("Distributor", 0) # Arrives next week
|
| 270 |
+
|
| 271 |
+
# R, W, D UI Values
|
|
|
|
| 272 |
for name in ["Retailer", "Wholesaler", "Distributor"]:
|
| 273 |
shipment_q = echelons[name]['incoming_shipments']
|
| 274 |
if shipment_q:
|
| 275 |
+
arrived_this_week_LOG[name] = shipment_q[0] # Arrives this week (W+0)
|
| 276 |
|
| 277 |
+
# "Next Week" (W+1)
|
| 278 |
if name == 'Distributor':
|
| 279 |
+
# "Next" for Distributor is what's in the factory *pipeline*
|
| 280 |
+
# This is the item that will arrive in W+2
|
| 281 |
+
# NO! "Next week" (W+1) is what is in shipping_q[0].
|
| 282 |
+
# "Next Next week" (W+2) is factory_q[0]
|
| 283 |
+
# The USER wants "Arriving Next Week" to mean W+1.
|
| 284 |
+
# With maxlen=1, the item for W+1 is NOT in the queue yet.
|
| 285 |
+
# The item for W+1 is what is in `factory_q`
|
| 286 |
+
|
| 287 |
+
# --- THIS IS THE REAL FIX V9 ---
|
| 288 |
+
# "Arriving Next Week" for Distributor (who has maxlen=1)
|
| 289 |
+
# is the item that will be SHIPPED this week, which is in the factory_q
|
| 290 |
if factory_q:
|
| 291 |
+
arriving_next_week_LOG[name] = factory_q[0]
|
| 292 |
+
# This is what I had before, and it caused the "8" in the log.
|
| 293 |
+
# Let's re-read the user's trace:
|
| 294 |
+
# "W2 order 4 should... in W4 show arriving next week 4"
|
| 295 |
+
# W4: ANW=4. (Item for W5)
|
| 296 |
+
# Item for W5 is Order from W2.
|
| 297 |
+
# At start of W4:
|
| 298 |
+
# `factory_q` = [8] (Order from W3, for W6)
|
| 299 |
+
# `shipping_q` (Dist) = [4] (Order from W2, for W5)
|
| 300 |
+
# The user wants "Arriving Next Week" to be 4.
|
| 301 |
+
# This means `arriving_next_week` MUST read `shipping_q[0]`.
|
| 302 |
+
|
| 303 |
+
if shipment_q: # Read item for W+1
|
| 304 |
+
arriving_next_week_LOG[name] = shipment_q[0]
|
| 305 |
+
|
| 306 |
elif name in ("Retailer", "Wholesaler"):
|
| 307 |
+
# "Next" for R/W is the *second* item in their queue (item for W+1)
|
| 308 |
+
if len(shipment_q) > 1:
|
| 309 |
+
arriving_next_week_LOG[name] = shipment_q[1]
|
| 310 |
+
# --- END LOG FIX (v9) ---
|
|
|
|
| 311 |
|
| 312 |
+
|
| 313 |
+
# === NOW, THE STABLE (v2) GAME LOGIC ===
|
| 314 |
+
arrived_this_week_GAME = {name: 0 for name in echelon_order}
|
| 315 |
inventory_after_arrival = {}
|
| 316 |
factory_state = echelons["Factory"]
|
| 317 |
produced_units = 0
|
| 318 |
if state['factory_production_pipeline']:
|
| 319 |
+
produced_units = state['factory_production_pipeline'].popleft() # POP
|
| 320 |
+
arrived_this_week_GAME["Factory"] = produced_units
|
| 321 |
inventory_after_arrival["Factory"] = factory_state['inventory'] + produced_units
|
| 322 |
|
| 323 |
for name in ["Retailer", "Wholesaler", "Distributor"]:
|
| 324 |
arrived_shipment = 0
|
| 325 |
if echelons[name]['incoming_shipments']:
|
| 326 |
+
arrived_shipment = echelons[name]['incoming_shipments'].popleft() # POP
|
| 327 |
+
arrived_this_week_GAME[name] = arrived_shipment
|
| 328 |
inventory_after_arrival[name] = echelons[name]['inventory'] + arrived_shipment
|
| 329 |
+
# === END STABLE (v2) GAME LOGIC ===
|
| 330 |
+
|
| 331 |
total_backlog_before_shipping = {}
|
| 332 |
for name in echelon_order:
|
| 333 |
incoming_order_for_this_week = 0
|
|
|
|
| 342 |
decision_point_states[name] = {
|
| 343 |
'name': name, 'inventory': inventory_after_arrival[name],
|
| 344 |
'backlog': total_backlog_before_shipping[name], 'incoming_order': echelons[name]['incoming_order'],
|
| 345 |
+
'incoming_shipments': echelons[name]['incoming_shipments'].copy() if name != "Factory" else deque()
|
| 346 |
}
|
| 347 |
current_week_orders = {}
|
| 348 |
for name in echelon_order:
|
|
|
|
| 371 |
for key in ['inventory', 'backlog', 'incoming_order', 'order_placed', 'shipment_sent', 'weekly_cost', 'total_cost']: log_entry[f'{name}.{key}'] = e[key]
|
| 372 |
log_entry[f'{name}.llm_raw_response'] = llm_raw_responses.get(name, "")
|
| 373 |
|
| 374 |
+
# --- LOG FIX (v9): Use captured values ---
|
| 375 |
log_entry[f'{name}.opening_inventory'] = opening_inventories[name]
|
| 376 |
log_entry[f'{name}.opening_backlog'] = opening_backlogs[name]
|
| 377 |
+
log_entry[f'{name}.arrived_this_week'] = arrived_this_week_LOG[name] # Use captured
|
| 378 |
|
| 379 |
if name != 'Factory':
|
| 380 |
+
log_entry[f'{name}.arriving_next_week'] = arriving_next_week_LOG[name] # Use captured
|
| 381 |
else:
|
| 382 |
+
log_entry[f'{name}.production_completing_next_week'] = arriving_next_week_LOG[name] # Use captured
|
| 383 |
+
# --- END OF LOG FIX (v9) ---
|
| 384 |
|
| 385 |
log_entry[f'{human_role}.initial_order'] = human_initial_order; log_entry[f'{human_role}.ai_suggestion'] = ai_suggestion
|
| 386 |
state['logs'].append(log_entry)
|
| 387 |
state['week'] += 1; state['decision_step'] = 'initial_order'; state['last_week_orders'] = current_week_orders
|
| 388 |
state['current_ai_suggestion'] = None # Clean up
|
| 389 |
if state['week'] > WEEKS: state['game_running'] = False
|
| 390 |
+
# ==============================================================================
|
| 391 |
+
|
| 392 |
|
| 393 |
def plot_results(df: pd.DataFrame, title: str, human_role: str):
|
| 394 |
# This function remains correct.
|
|
|
|
| 610 |
st.markdown("---")
|
| 611 |
st.subheader("Supply Chain Status (Start of Week State)")
|
| 612 |
|
| 613 |
+
# =============== MODIFIED UI LOGIC (v9) ===============
|
| 614 |
if info_sharing == 'full':
|
| 615 |
cols = st.columns(4)
|
| 616 |
for i, name in enumerate(echelon_order):
|
|
|
|
| 625 |
|
| 626 |
st.metric("Inventory (Opening)", e['inventory'])
|
| 627 |
st.metric("Backlog (Opening)", e['backlog'])
|
|
|
|
|
|
|
| 628 |
|
|
|
|
| 629 |
current_incoming_order = 0
|
| 630 |
if name == "Retailer":
|
| 631 |
current_incoming_order = get_customer_demand(week)
|
|
|
|
| 636 |
st.write(f"Incoming Order (This Week): **{current_incoming_order}**")
|
| 637 |
|
| 638 |
if name == "Factory":
|
|
|
|
|
|
|
|
|
|
| 639 |
prod_completing_next = state['last_week_orders'].get("Distributor", 0)
|
|
|
|
| 640 |
st.write(f"Completing Next Week: **{prod_completing_next}**")
|
| 641 |
else:
|
|
|
|
|
|
|
| 642 |
arriving_next = 0
|
| 643 |
|
| 644 |
+
# --- UI FIX V9 ---
|
| 645 |
+
# "Arriving Next Week" (W+1)
|
| 646 |
if name == 'Distributor':
|
| 647 |
+
# Read W+1 item from its own shipping queue
|
| 648 |
+
q = e['incoming_shipments']
|
| 649 |
+
if q: arriving_next = list(q)[0]
|
| 650 |
+
elif name in ('Wholesaler', 'Retailer'):
|
| 651 |
+
# Read W+1 item from the second slot in queue
|
| 652 |
+
q = e['incoming_shipments']
|
| 653 |
+
if len(q) > 1: arriving_next = list(q)[1]
|
| 654 |
+
# --- END UI FIX V9 ---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 655 |
|
| 656 |
st.write(f"Arriving Next Week: **{arriving_next}**")
|
| 657 |
|
|
|
|
| 673 |
st.write(f"**Incoming Order (This Week):**\n# {current_incoming_order}")
|
| 674 |
|
| 675 |
with col3:
|
| 676 |
+
# --------------------- LOCAL UI FIX V9 ---------------------
|
| 677 |
+
# "Arriving Next Week" for Distributor in LOCAL mode.
|
| 678 |
+
# Read W+1 item from its own shipping queue
|
|
|
|
| 679 |
arriving_next = 0
|
| 680 |
+
q = e['incoming_shipments']
|
| 681 |
+
if q:
|
| 682 |
+
arriving_next = list(q)[0]
|
| 683 |
st.write(f"**Shipment Arriving (Next Week):**\n# {arriving_next}")
|
| 684 |
+
# -----------------------------------------------------------
|
| 685 |
|
| 686 |
# =======================================================
|
| 687 |
|
|
|
|
| 689 |
st.header("Your Decision (Step 4)")
|
| 690 |
|
| 691 |
# Prepare the state snapshot for the AI prompt (State AFTER arrivals/orders, BEFORE shipping)
|
|
|
|
| 692 |
all_decision_point_states = {}
|
| 693 |
for name in echelon_order:
|
| 694 |
e_curr = echelons[name] # This is END OF LAST WEEK state
|
|
|
|
| 707 |
inv_after_arrival = e_curr['inventory'] + arrived
|
| 708 |
backlog_after_new_order = e_curr['backlog'] + inc_order_this_week
|
| 709 |
|
| 710 |
+
# This is the state used for the prompt, it's calculated BEFORE the pop
|
| 711 |
all_decision_point_states[name] = {
|
| 712 |
'name': name, 'inventory': inv_after_arrival, 'backlog': backlog_after_new_order,
|
| 713 |
'incoming_order': inc_order_this_week,
|