A hybrid GA approach to the scheduling of machines and automated guided vehicles in flexible job shops

Abstract:

This work proposes a hybrid genetic algorithm based on timetable and A* algorithm to solve the scheduling problem of multiautomatic guided vehicle (AGV) distribution in the flexible workshop, and the optimization goal of the algorithm is to minimize the total time for the AGV to transport the workpieces from the rough library to the finished product warehouse. Two schemes are proposed to solve the collision problem in AGV path planning and the problem of position occupation while the AGV is waiting at the position of machine. The machine and AGV scheduling is integrated in the divided task unit. Then the chromosome coding method based on the task unit is designed. The population initialization scheme, the cross and mutation operator and the elite retention strategy are improved. In the decoding operation, according to the schedule information, the A* algorithm and conflict resolution are used to plan out the best path without collisions and occupation conflicts in each task unit. Finally, the feasibility and effectiveness of the algorithm are verified by the numerical examples.