commit

network_diagram.py

@@ -0,0 +1,76 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from collections import defaultdict
+from create_optimization_problem import create_sourcing_problem
+from solve_optimization_problem import solve_sourcing_problem
+from read_data import load_excel_data
+
+
+def create_sourcing_graph(fulfillment_solution):
+    # Create a directed graph
+    G = nx.DiGraph()
+
+    # Determine warehouses and orders from shipments
+    warehouses = set(w for w, _, _, _ in fulfillment_solution)
+    orders = set(o for _, _, o, _ in fulfillment_solution)
+
+    # Add nodes to graph
+    G.add_nodes_from(warehouses, color='blue')  # Warehouses
+    G.add_nodes_from(orders, color='red')  # Orders
+
+    # Group shipments by (warehouse, order)
+    shipment_info = defaultdict(list)
+    for w, p, o, q in fulfillment_solution:
+        shipment_info[(w, o)].append(f"{p} ({q})")
+
+    # Add edges with combined product info as labels
+    edge_labels = {}
+    edge_widths = []
+    edge_colors = []
+    for (w, o), p in shipment_info.items():
+        label = "\n".join(p)  # Combine all products for the edge
+        G.add_edge(w, o, label=label)
+        edge_labels[(w, o)] = label
+        total_qty = sum(int(p.split('(')[-1].strip(')')) for p in p)  # Sum quantities
+        edge_widths.append(total_qty / 10)  # Scale edge width by total quantity
+        edge_colors.append("black")
+
+    # Positioning the nodes
+    pos = nx.spectral_layout(G)
+
+    # Node colors
+    node_colors = ["yellow" if node in warehouses else "red" for node in G.nodes]
+
+    # Draw the network
+    plt.figure(figsize=(8, 6))
+    nx.draw(G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors, 
+            node_size=2000, font_size=10, width=edge_widths, arrows=True)
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=8, label_pos=0.5)
+
+    # Add legend
+    warehouse_patch = mpatches.Patch(color="blue", label="Warehouse")
+    order_patch = mpatches.Patch(color="red", label="Order")
+    plt.legend(handles=[warehouse_patch, order_patch], loc="upper right")
+
+    plt.title("Enhanced Sourcing Network Diagram")
+    plt.show()
+
+    plt.savefig('sourcing_network.png')
+
+
+
+if __name__ == "__main__":
+    filepath = 'Intelligent_Sourcing.xlsx'  # Example file path
+    weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df,  = load_excel_data(filepath)
+    
+    # Create LP Problem
+    prob, Warehouses, Products, Stock, Priority, Orders, Quantity, Variable = create_sourcing_problem(weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df)
+    #print(prob.objective)
+
+    # Solve LP Problem
+    status, fulfillment_solution, warehouse_stock_status = solve_sourcing_problem(prob, Warehouses, Products, Stock, Priority, Orders, Quantity, Variable)
+
+    print(fulfillment_solution)
+    
+    create_sourcing_graph(fulfillment_solution.itertuples(index=False, name=None))

solve_optimization_problem.py

@@ -67,8 +67,6 @@ if __name__ == "__main__":
     filepath = 'Intelligent_Sourcing.xlsx'  # Example file path
     weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df,  = load_excel_data(filepath)
     
-    prob, Warehouses, Products, Stock, Priority, Orders, Quantity, Variable = create_sourcing_problem(weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df)
-    
     # Create LP Problem
     prob, Warehouses, Products, Stock, Priority, Orders, Quantity, Variable = create_sourcing_problem(weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df)
     #print(prob.objective)