A particle swarm optimization 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 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. In this paper, a novel real number encoding method of Particle Swarm Optimization (PSO) for Open Vehicle Routing Problem is proposed. The vehicle is mapped into the integer part of the real number; and the sequence of customers in the vehicle is mapped into the decimal fraction of the real number. They are used to optimize the inner or outer routes and modify illegal solutions. 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.