Graph Theory Math Ia 📥

1. Introduction Aim: To determine the most efficient (shortest) route for a delivery driver in a local suburban network using graph theory, and to compare the effectiveness of Dijkstra’s algorithm against simple visual inspection.

| Edge | Weight | Edge | Weight | |------|--------|------|--------| | S–A | 200 | B–C | 180 | | S–B | 350 | C–D | 120 | | A–B | 150 | C–E | 250 | | A–C | 400 | D–F | 300 | | B–D | 220 | E–F | 100 | | B–E | 280 | F–T | 90 | | A–D | 310 | D–T | 500 (direct but long) | graph theory math ia

Unvisited min = B(350). Current = B. Neighbors: S(no), A(350+150=500 vs 200 no), C(350+180=530 vs 600 → update C=530), D(350+220=570 vs 510 no), E(350+280=630). Visited S,A,B. Current = B

I used Google Maps for weights and could extend to Excel/Python. I used Google Maps for weights and could

Unvisited min = A(200). Current = A. Neighbors: S(200+200 no better), B(200+150=350 vs current 350 tie), C(200+400=600), D(200+310=510). Update: C=600, D=510. Visited S,A.