Original Vehicle Routing Problem Solver Using Genetic Algorithm

This is my original implementation of a Vehicle Routing Problem (VRP) solver using genetic algorithms, developed in MATLAB programming language.

Genetic Algorithm is an optimization technique that mimics natural selection and genetic mechanisms from biological evolution to solve complex problems. In this project, I applied genetic algorithms to address the Vehicle Routing Problem (VRP), which involves finding optimal delivery routes for multiple vehicles serving a set of customers. The VRP challenge requires determining the most efficient set of routes that satisfy all customer demands while minimizing total travel distance or overall cost constraints. My MATLAB-based solution leverages genetic algorithm advantages including chromosome encoding of routes, fitness function evaluation measuring route efficiency, selection operators favoring better solutions, crossover operations for route recombination, and mutation mechanisms to maintain population diversity. Through iterative optimization cycles and parameter tuning, I successfully developed an efficient VRP solving tool that demonstrates robust performance in finding near-optimal routing configurations while handling various problem constraints.