A Hybrid Intelligent Algorithm for Grain Logistics Vehicle Routing Problem

Vehicle routing problems (VRP) arise in many real-life applications within transportation and logistics. This paper considers vehicle routing models in grain logistics (GLVRP) and its hybrid intelligent algorithm. The objective of GLVRP is to use a fleet of vehicles with specific capacity to serve a number of customers with fixed demand and time window constraints. A hybrid intelligent algorithm base on particle swarm and ant colony optimization (PSACO-GLVRP) is proposed to solve this problem. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In the experiments, a number of numerical examples are carried out for testing and verification. The Computational results confirm the efficiency of the proposed methodology.