Improved Ant Colony Algorithm for Logistics Vehicle Routing Problem with Time Window

Abstract

For the current ant colony algorithm(ACO) cannot take the real-time road condition into consideration, a improved ACO was proposed which covers there aspects influence of people, vehicle and road. Mathematical model of driver effect factor was put forward. Define the road network division algorithm. Consider the vehicle’s condition. To avoid the ACO slide into local optima, negative feedback strategy was introduced when updating the global pheromone. Time window impact factor was added into the logistics vehicle’s transition probability algorithm to make sure that the rush order have a relatively higher priority to be processed. The improved ACO aimed at solving vehicle routing problem(VRP) was accomplished by the computer. Test results show that the improved ACO has better optimization efficiency.