Conflict-free scheduling algorithm for multiple AGVs in smart factories

The development of artificial intelligence technology makes industrial production transform to intelligence, and AGVs play an important role in the material transportation process of intelligent factories. In this paper, we aim at the task assignment and path planning problem in the AGV scheduling link, and design a method of collaborative optimization between task assignment and path planning, and the main work is reflected in three points: 1) Establish a mixed integer linear programming model for the AGV scheduling problem in industrial warehouses to minimize the maximum task completion time as the optimization objective, and formulate the corresponding constraints; 2) improve the path planning heuristic function, improve the traditional distance heuristic to a time heuristic, so that the path optimization process is adapted to the mathematical model based on time optimization, and improve the search efficiency; 3) improve the mutation strategy, introduce a global search operator and enhance the local search operator, and take full advantage of the diversity of the information of population Adjust the search direction of the algorithm so that the algorithm can jump out of the local search limitations and improve the solution quality. The experimental results show that compared with the adaptive differential evolution algorithm with external archive set, the algorithm in this paper reduces the task completion time by 3.7%, obtains a better quality and quantity of the task assignment scheme, and ensures that the traveling paths of AGVs are conflict-free.