A Multi-AGV Task Scheduling Method Based on a Hybrid Adaptive Nearest Neighbor Heuristic Algorithm and Genetic Algorithm

Automated Guided Vehicles (AGVs) are essential transportation tools in modern industrial production, and their scheduling directly affects workshop efficiency. The AGV scheduling problem (AGVSP) aims to minimize both transportation costs and penalty costs while ensuring the completion of tasks. To address this challenge, this paper introduces a multi-AGV task scheduling method that combines a hybrid adaptive nearest neighbor heuristic algorithm with a genetic algorithm (ANNH-GA). Initially, we propose an improved adaptive nearest neighbor heuristic algorithm to generate the initial population. Next, we design a fitness function tailored to address time and path conflicts specific to AGV scheduling, thereby enhancing the reflection of global optimality in task scheduling. Finally, simulation experiments demonstrate that the ANNH-GA algorithm outperforms several commonly used task scheduling algorithms.