Aiming at the influence of AGV without considering the working state on task assignment decision in multi-AGV system task assignment, a dynamic task assignment decision method with task completion prediction based on genetic algorithm. When assigning the arrived tasks at each stage, the decision method brings the working AGVs and the idle AGVs into the set of schedulable vehicles at the same time, which expands the scope of the optimal decision, makes the available AGV resources more fully mobilized in the dynamic scheduling process, and improves the efficiency of the whole scheduling system. First, this paper establishes a prediction model for task completion. On this basis, the task assignment decision model of multi-AGV system based on task completion prediction is established, and the coding, fitness function and genetic operation of the genetic algorithm suitable for this problem are designed. Finally, a univariate factor analysis is carried out on the task assignment time interval and the number of AGVs by using an example, which verifies the effectiveness of the task assignment strategy of the multi-AGV system based on task completion prediction. The results show that the genetic algorithm can better solve the task assignment problem with task completion prediction, and can schedule the available AGV resources to a greater extent, which effectively increase the number of tasks completed by the multi-AGV system in one production cycle.
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