Hybrid Genetic and Simulated Annealing Algorithm for Capacitated Vehicle Routing Problem

The vehicle routing problem is a well-known combinatorial optimization problem and its optimization has impact on various domains including smart logistics, smart cities, unmanned air vehicle routing and others. In Capacitated Vehicle Routing Problem (CVRP), the known demands of customers are fulfilled by identical vehicles with objective to optimize the cost in terms of distance. In this work, we propose to solve CVRP using Hybrid Genetic and Simulated Annealing (HGSA) Algorithm to optimize the total travelled distance. The proposed HGSA algorithm combines genetic algorithm and simulated annealing to search global optimal solutions. The HGSA algorithm employs novel nearest-neighbor crossover operator which generates solutions based on nearest-neighbors so that the total travelled distance remains minimum possible. The proposed HGSA Algorithm was tested with 86 benchmark CVRP instances and the effectiveness of HGSA is shown by the results offered.