Abstract: With the rapid development of E-commerce and artificial intelligence technology, the scheduling optimization problem is studied for AGV(Automated Guided Vehicle)in the intelligent warehouse which uses“cargo-to-picker” picking system. This problem simultaneously considers two sub-problems: the AGV route generating and routes collision avoidance between multiple AGVs. To solve the problem, a two-phase optimization approach is present. First, two methods are developed, i.e., a mathematical model and a rack-priority-based method to allocate the rack-moving tasks to AGVs. Based on the task allocation results, the path generation algorithm is developed to get the AGV initial feasible path. Then, a collision avoidance algorithm is designed to obtain collision-free AGV routes considering possible collision path points. It is further considered unexpected disruptions occur, such as equipment breakdown. Therefore, a real-time rescheduling method is developed to obtain AGV collision-free path in the warehouse. The performance of the two-stage optimization algorithm exploring the two rack allocation methods are evaluated by computational experiments. The results show that this algorithm can help to obtain an optimized AGV schedule in a warehouse with 2015 shelves, 10 operating platforms, 100 AGVs and 100 orders in a batch. The algorithm also can give suggestions of optimal configuration of AGV quantity to give a sized warehouse. The study not only can provide a theoretical model and algorithm for scheduling operation of multiple AGVs, but also can provide practical guidance for enterprises exploring the “cargo-to-picker” picking system.

Key words: intelligent warehouse；AGV schedule；dynamic collision avoidance；“cargo-to-picker” picking