Problem FedEx must transport packages from a central warehouse to various destinations (hubs) using a road network. Each road segment has an associated cost, representing the transportation cost per kilometer. Problem Facts 1. Central warehouse at node A, with four destinations (nodes B, C, D, and E). 2. Transportation costs per kilometer between nodes: -A to B: $5 USD -A to C: $3 USD -A to D: $8 USD -B to E: $2 USD -C to B: $2 USD -C to D: $2 USD -D to E: $4 USD Tasks 1. Modeling the Problem: Represent the problem using a weighted directed graph in Python with the networkx library, add nodes for the central warehouse and destinations and add weighted edges to represent the roads and their costs. 2. Shortest Path Search: Apply two shortest path search algorithms (Dijkstra and Bellman-Ford) to find the shortest path from the central warehouse to each destination and record the optimal paths and associated costs. Network visualization: Use the networkx package to visualize the network, mark in a different color (e.g. red) the shortest routes found for better visualization.
Problem
FedEx must transport packages from a central warehouse to various destinations (hubs) using a road network. Each road segment has an associated cost, representing the transportation cost per kilometer.
Problem Facts
1. Central warehouse at node A, with four destinations (nodes B, C, D, and E).
2. Transportation costs per kilometer between nodes:
-A to B: $5 USD
-A to C: $3 USD
-A to D: $8 USD
-B to E: $2 USD
-C to B: $2 USD
-C to D: $2 USD
-D to E: $4 USD
Tasks
1. Modeling the Problem: Represent the problem using a weighted directed graph in Python with the networkx library, add nodes for the central warehouse and destinations and add weighted edges to represent the roads and their costs.
2. Shortest Path Search: Apply two shortest path search
Network visualization: Use the networkx package to visualize the network, mark in a different color (e.g. red) the shortest routes found for better visualization.
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