Update agents/reasoner.py

agents/reasoner.py

@@ -84,7 +84,7 @@ def run_picking_optimization(message, picking_df):
 
 
 # ============================================================
-#  DEMAND FORECASTING — PHASE 3 MODULE 1
+#  DEMAND FORECASTING — MODULE 1
 # ============================================================
 
 def run_demand_forecast(message, slotting_df):
@@ -114,6 +114,7 @@ def run_demand_forecast(message, slotting_df):
 
     reasoning_steps.append("Generated 7-day demand projection.")
 
+    # Plot forecast
     plt.figure(figsize=(6, 4))
     plt.plot(forecast_df["Day"], forecast_df["Forecasted_Demand"], marker="o")
     plt.title("7-Day Demand Forecast")
@@ -135,7 +136,7 @@ def run_demand_forecast(message, slotting_df):
 
 
 # ============================================================
-#  REPLENISHMENT OPTIMIZATION — PHASE 3 MODULE 2
+#  REPLENISHMENT OPTIMIZATION — MODULE 2
 # ============================================================
 
 def run_replenishment_analysis(message, slotting_df):
@@ -170,3 +171,130 @@ def run_replenishment_analysis(message, slotting_df):
     )
 
     return explanation, df
+
+
+# ============================================================
+#  INVENTORY REBALANCING — MODULE 3
+# ============================================================
+
+def run_rebalancing_analysis(message, slotting_df):
+    reasoning = []
+    df = slotting_df.copy()
+
+    if "Frequency" not in df.columns:
+        return "Frequency missing — cannot rebalance.", None
+
+    if "Aisle" not in df.columns:
+        df["Aisle"] = np.arange(1, len(df) + 1)
+        reasoning.append("Aisle data missing — assigned aisles automatically.")
+
+    velocity_map = {"fast": 3, "medium": 2, "slow": 1}
+    df["VelScore"] = df["Velocity"].str.lower().map(velocity_map)
+    df["LoadScore"] = df["VelScore"] * 0.6 + df["Frequency"] * 0.4
+    reasoning.append("Calculated SKU load score (velocity + frequency).")
+
+    aisle_load = df.groupby("Aisle")["LoadScore"].sum().reset_index()
+    avg_load = aisle_load["LoadScore"].mean()
+
+    aisle_load["Congestion"] = aisle_load["LoadScore"].apply(
+        lambda x: "🔴 High" if x > avg_load * 1.25
+        else ("🟡 Medium" if x > avg_load * 0.75 else "🟢 Low")
+    )
+
+    df = df.merge(aisle_load[["Aisle", "Congestion"]], on="Aisle", how="left")
+    reasoning.append("Assigned congestion levels to aisles.")
+
+    high_aisles = aisle_load[aisle_load["Congestion"] == "🔴 High"]["Aisle"].tolist()
+    low_aisles = aisle_load[aisle_load["Congestion"] == "🟢 Low"]["Aisle"].tolist()
+
+    move_plan = []
+
+    if high_aisles and low_aisles:
+        for aisle in high_aisles:
+            congested_skus = df[df["Aisle"] == aisle].sort_values("LoadScore", ascending=False)
+            top_to_move = congested_skus.head(2)
+
+            for i, row in top_to_move.iterrows():
+                target_aisle = low_aisles[i % len(low_aisles)]
+                move_plan.append({
+                    "SKU": row["SKU"],
+                    "FromAisle": row["Aisle"],
+                    "ToAisle": target_aisle,
+                    "LoadScore": round(row["LoadScore"], 2),
+                    "Reason": "Reduce congestion"
+                })
+
+        reasoning.append("Generated SKU redistribution plan.")
+    else:
+        reasoning.append("No congestion found — no rebalancing needed.")
+
+    move_df = pd.DataFrame(move_plan)
+
+    explanation = (
+        "### 🔄 Inventory Rebalancing\n"
+        "SKU redistribution plan to reduce aisle congestion.\n\n"
+        "#### 🔍 Reasoning\n" + "\n".join([f"- {r}" for r in reasoning])
+    )
+
+    return explanation, move_df
+
+
+# ============================================================
+#  WORKFORCE OPTIMIZATION — MODULE 4
+# ============================================================
+
+def run_workforce_optimization(message, slotting_df):
+    """
+    Estimate workforce needed based on SKU demand load.
+    """
+    reasoning = []
+    df = slotting_df.copy()
+
+    if "Frequency" not in df.columns:
+        return "Cannot calculate workforce — missing Frequency column.", None
+
+    df["Workload"] = df["Frequency"] * 1.2  # weight multiplier
+    total_workload = df["Workload"].sum()
+    workers_needed = max(1, int(total_workload // 150))
+
+    reasoning.append(f"Total workload: {total_workload:.2f}")
+    reasoning.append(f"Workers required (estimated): {workers_needed}")
+
+    result = pd.DataFrame({
+        "Metric": ["Total Workload", "Estimated Workers Needed"],
+        "Value": [total_workload, workers_needed]
+    })
+
+    explanation = (
+        "### 👷 Workforce Optimization\n"
+        "Estimated staffing requirement based on SKU workload.\n\n"
+        "#### 🔍 Reasoning\n" + "\n".join([f"- {r}" for r in reasoning])
+    )
+
+    return explanation, result
+
+
+# ============================================================
+#  DOCK SCHEDULING OPTIMIZATION — MODULE 5
+# ============================================================
+
+def run_dock_scheduling(message, slotting_df):
+    """
+    Simple dock scheduling:
+    - Assigns SKUs to docks based on demand priority
+    """
+    reasoning = []
+    df = slotting_df.copy()
+
+    df["Priority"] = df["Frequency"].rank(ascending=False)
+    df["AssignedDock"] = df["Priority"].apply(lambda x: int((x - 1) % 3) + 1)
+
+    reasoning.append("Assigned SKUs to 3 docks based on priority rank.")
+
+    explanation = (
+        "### 🚛 Dock Scheduling Optimization\n"
+        "SKUs allocated to dock doors based on priority.\n\n"
+        "#### 🔍 Reasoning\n" + "\n".join([f"- {r}" for r in reasoning])
+    )
+
+    return explanation, df[["SKU", "Frequency", "Priority", "AssignedDock"]]