Solving Vehicle Routing Problem Using Genetic Algorithm

This program utilizes genetic algorithms to solve vehicle routing problems (VRP). It optimizes vehicle routes to minimize both transportation time and operational costs. The genetic algorithm mimics natural selection and genetic mechanisms, evolving population solutions through selection, crossover, and mutation operations. In this implementation, the algorithm handles route optimization through chromosome encoding representing vehicle paths, fitness functions calculating total distance/cost, and genetic operators that iteratively improve solution quality. The program ensures completeness through features including population initialization, elitism preservation, and termination conditions, while its robustness comes from thorough constraint handling (capacity, time windows) and adaptive parameter tuning. The code architecture guarantees reliability with modular components for distance calculation, route validation, and performance metrics tracking, making it practically applicable for real-world logistics optimization scenarios.