Multi-AGV Task Allocation Strategy Based on Improved Particle Swarm Optimization Algorithm

Efficient task allocation for multiple automated guided vehicles (AGVs) in factory environments is one of the key issues in intelligent manufacturing. This study constructs a mathematical model with the goal of minimizing the total running time between delivery tasks, and optimizes it through an improved particle swarm optimization algorithm. The model first calculates the distance matrix from AGV to priority tasks, formulates a distance based priority task allocation scheme, and initializes particle positions. In addition, the model introduces a penalty term for task allocation balance in the evaluation function of the algorithm to reduce the inefficiency caused by imbalanced allocation. Through simulation experiments on 30 sets of tasks and 5 AGVs, it has been proven that the algorithm can effectively reduce the total running time when processing priority tasks, thus verifying the effectiveness of the method.