A multi-objective ant colony algorithm for the optimization of path planning problem with time window

The vehicle routing problem is a typical problem in combinatorial optimization, which has a wide range of applications in various industrial and engineering areas. Ant Colony Optimization which is a heuristic algorithm is a victorious technique to solve complicated optimized problems. Traditionally, the distance of the route is the only objective to be considered in the vehicle path planning problem. Due to complex road conditions and uncertain factors, it has become essential to adopt the efficient measure of obtaining the optimal path to save time and resources. In this paper, an improved ant colony optimization algorithm is proposed to select the optimal path for the single-vehicle path planning problem with time windows under uncertain transportation times. To be specific, two penalty approaches are adopted in the algorithm to evaluate the route which is chosen by ants. The experimental results show that the proposed algorithm is highly effective in randomly generating up to 200 customer locations and path information.