MATLAB Code Implementation for Solving Vehicle Routing Problem (VRP)

This document presents a MATLAB-based solution for Vehicle Routing Problems (VRP) using genetic algorithms. The implemented routine solves basic VRP scenarios through evolutionary computation techniques. The VRP.M file contains predefined parameters including: the distance matrix between supply points, population size, crossover rate, mutation rate, and maximum generations. The genetic algorithm employs route length as the fitness function, with constraint violations handled by incorporating penalty factors in the LEN calculation function. For implementation simplicity, infeasible routes that violate constraints receive a fixed penalty of 100 distance units added to their total length. The chromosome representation encodes vehicle routes as sequences of customer indices, while specialized genetic operators ensure valid route formation. Beyond providing executable VRP solution code, this documentation elaborates on genetic algorithm fundamentals including population initialization, tournament selection, ordered crossover, and swap mutation mechanisms tailored for routing problems. The content also discusses typical VRP applications in logistics optimization and supply chain management. This comprehensive guide aims to facilitate understanding of both MATLAB programming techniques and evolutionary algorithm applications for complex combinatorial optimization problems.