Supply Chain and Inventory Management – Route Optimization Engine

 

Notes:

• Problem Solved: Finds the most efficient delivery routes, reducing fuel costs and delivery times.

• Benefits: Businesses can lower logistics costs and improve customer satisfaction through faster delivery.

• Adoption: Can be integrated with delivery dispatch systems or used to evaluate alternative route plans.

Python Code:

import networkx as nx

class RouteOptimizer:

    def __init__(self):

        self.graph = nx.Graph()

    def add_route(self, source, destination, distance):

        self.graph.add_edge(source, destination, weight=distance)

    def find_shortest_path(self, start, end):

        return nx.shortest_path(self.graph, source=start, target=end, weight='weight')

    def path_distance(self, path):

        return sum(self.graph[u][v]['weight'] for u, v in zip(path[:-1], path[1:]))

# Define routes

optimizer = RouteOptimizer()

routes = [

    ('Warehouse', 'CityA', 10),

    ('Warehouse', 'CityB', 15),

    ('CityA', 'CityC', 12),

    ('CityB', 'CityC', 10),

    ('CityC', 'Customer', 5)

]

for route in routes:

    optimizer.add_route(*route)

# Find shortest path from warehouse to customer

shortest_path = optimizer.find_shortest_path('Warehouse', 'Customer')

distance = optimizer.path_distance(shortest_path)

print(f"Shortest delivery path: {shortest_path} with distance: {distance}")