Solving VRP Vehicle Routing Problem Using GA Genetic Algorithm

This solution utilizes Genetic Algorithm (GA) to address the Vehicle Routing Problem (VRP) for distribution optimization, including its special case - the Traveling Salesman Problem (TSP). The implementation involves chromosome encoding representing route sequences, fitness functions calculating total distance, and genetic operators like crossover and mutation for population evolution. In VRP applications, the algorithm determines optimal vehicle routes to fulfill all delivery requirements while considering constraints such as delivery time windows and vehicle capacity limits. The code typically includes constraint handling mechanisms through penalty functions or specialized genetic operators to maintain solution feasibility. TSP represents a simplified scenario where the algorithm finds the shortest possible route visiting all cities exactly once, without delivery demands and vehicle capacity constraints. The implementation often uses permutation-based chromosome representation and specialized crossover operators like Ordered Crossover (OX) or Partially Mapped Crossover (PMX). Through genetic algorithm simulation of natural selection and genetic mutation processes, the solution iteratively optimizes vehicle routes toward optimal solutions. Key implementation components include population initialization, selection mechanisms (roulette wheel, tournament selection), and convergence criteria monitoring for termination conditions. The algorithm efficiently explores solution space while maintaining population diversity through mutation probability adjustments.