目的 针对中小型智能生产车间物料搬运过程中任务分配不合理、资源利用率低等问题,构建以AGV任务完成时间最短、能耗最小和负载均衡为优化目标的多目标优化模型。方法 为提升求解效率与解的质量,提出一种改进NSGA-III算法,采用多层编码结构简化解码过程,并结合非支配解分布动态生成参考点,以适应复杂帕累托前沿分布;同时,引入自适应变异与选择算子策略,强化算法的全局搜索能力与局部收敛性能。基于MATLAB平台,在AGV相同起点与不同起点2种作业场景下开展仿真实验。结果 所提方法在任务完成时间、能耗和负载均衡指标上均优于传统算法,任务完成时间分别减少13.9%与4.64%,能耗降低21.87%与15.45%,负载均衡指数下降39.3%与58.47%。结论 该方法有效提升了多AGV系统调度性能与作业效率。
Abstract
The work aims to construct a multi-objective optimization model with the shortest AGV task completion time, minimum energy consumption and load balance as the optimization objectives to address issues such as unreasonable task allocation and low resource utilization during material handling in small and medium-sized smart production workshops. In order to improve the solution efficiency and solution quality, an improved NSGA-III algorithm was proposed, which adopted a multi-layer coding structure to simplify the decoding process and combined the non-dominated solution distribution to dynamically generate the reference point to adapt to the complex Pareto frontier distribution; At the same time, adaptive mutation and selection operator strategies were introduced to strengthen the global search capability and local convergence performance of the algorithm. Based on the MATLAB platform, simulation experiments were carried out under two operation scenarios, namely, the same starting point and different starting points of AGVs. The results showed that the proposed method outperformed the traditional algorithm in terms of task completion time, energy consumption and load balancing indexes, with the task completion time reduced by 13.9% and 4.64%, energy consumption reduced by 21.87% and 15.45%, and load balancing index decreased by 39.3% and 58.47% respectively. In conclusion, this method effectively enhances the scheduling performance and operational efficiency of the multi-AGV system.
关键词
AGV /
任务分配 /
多目标优化 /
NSGA-III算法
Key words
AGV /
task allocation /
multi-objective optimization /
NSGA-III algorithm
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基金
北京印刷学院校级项目（KYCPT202513）
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