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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import io
# ============================================================
# SLOTING OPTIMIZATION — PHASE 1
# ============================================================
def run_slotting_analysis(message, slotting_df):
reasoning_steps = []
df = slotting_df.copy()
# ---------------------------------------------------------
# 1️⃣ Convert velocity to numerical weight
# ---------------------------------------------------------
velocity_map = {
"fast": 3,
"medium": 2,
"slow": 1
}
df["VelocityNorm"] = df["Velocity"].str.lower().map(velocity_map)
reasoning_steps.append("Mapped velocity categories to numerical weights.")
# ---------------------------------------------------------
# 2️⃣ Normalize frequency (usage)
# ---------------------------------------------------------
df["FreqNorm"] = (df["Frequency"] - df["Frequency"].min()) / (
df["Frequency"].max() - df["Frequency"].min() + 1e-8
)
reasoning_steps.append("Normalized frequency to 0–1 scale.")
# ---------------------------------------------------------
# 3️⃣ Compute Slotting Score
# ---------------------------------------------------------
df["Score"] = (0.6 * df["VelocityNorm"]) + (0.4 * df["FreqNorm"])
reasoning_steps.append("Computed weighted slotting score (60% velocity, 40% frequency).")
# ---------------------------------------------------------
# 4️⃣ Assign Aisles and Racks based on score
# ---------------------------------------------------------
df = df.sort_values("Score", ascending=False).reset_index(drop=True)
aisles = np.arange(1, len(df) + 1)
racks = np.linspace(1, 20, len(df)).astype(int)
df["Aisle"] = aisles
df["Rack"] = racks
reasoning_steps.append("Assigned optimal aisle & rack positions based on ranking.")
explanation = (
"### 📦 Slotting Optimization\n"
"The SKUs were evaluated using a weighted model combining velocity and picking frequency.\n"
"High-velocity & high-frequency items are placed closer to prime aisles.\n\n"
"#### 🔍 Key Reasoning Steps:\n"
+ "\n".join([f"- {r}" for r in reasoning_steps])
)
return explanation, df
# ============================================================
# PICKING ROUTE OPTIMIZATION — PHASE 1
# ============================================================
def run_picking_optimization(message, picking_df):
reasoning_steps = []
df = picking_df.copy()
# ---------------------------------------------------------
# 1️⃣ Convert Aisle–Rack to coordinates
# ---------------------------------------------------------
df["x"] = df["Aisle"]
df["y"] = df["Rack"]
reasoning_steps.append("Converted Aisle–Rack values into x–y coordinate grid.")
# ---------------------------------------------------------
# 2️⃣ Compute Manhattan distance
# ---------------------------------------------------------
df["Distance"] = df["x"].abs() + df["y"].abs()
df = df.sort_values("Distance").reset_index(drop=True)
reasoning_steps.append("Calculated Manhattan distance and sorted for optimal walk order.")
# ---------------------------------------------------------
# 3️⃣ Plot the route
# ---------------------------------------------------------
plt.figure(figsize=(6, 6))
plt.plot(df["x"], df["y"], marker="o", linestyle="-")
plt.title("Optimized Picking Route")
plt.xlabel("Aisle")
plt.ylabel("Rack")
# Save image to BytesIO buffer
buffer = io.BytesIO()
plt.savefig(buffer, format="png")
buffer.seek(0)
plt.close()
reasoning_steps.append("Generated optimized walking path visualization.")
explanation = (
"### 🚚 Picking Route Optimization\n"
"Using Manhattan distance and spatial ordering, an optimal walking sequence was generated.\n\n"
"#### 🔍 Key Reasoning Steps:\n"
+ "\n".join([f"- {r}" for r in reasoning_steps])
)
return explanation, buffer
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