铁道科学与工程学报2025，Vol.22Issue(5)：2089-2099,11.DOI:10.19713/j.cnki.43-1423/u.T20241113
基于贪心-遗传混合算法的RMFS动态货位分配优化研究
Optimization of dynamic pod location assignment in RMFS based on a greedy-genetic hybrid algorithm
摘要
Abstract
With the rapid development of e-commerce,the high timeliness of orders and frequent demand fluctuations are posing significant challenges to the order picking process of Robotic Mobile Fulfillment Systems.Due to the mobility of pods in RMFS,continuously adjusting pod locations during the picking process is an effective way to improve picking efficiency.This paper proposed a dynamic pod location assignment strategy to address the challenges of dynamic pod allocation in RMFS.The strategy took into account real-time fluctuations in product demand while balancing the workload across picking stations.A mathematical model was constructed to maximize pod allocation value,achieving optimal pod positioning.The improved algorithm was used to plan the picking paths of Automated Guided Vehicles,reducing the number of turns.To address the challenges of dynamic continuity and a vast solution space,a hybrid algorithm combining a greedy algorithm with an improved adaptive genetic algorithm was designed to solve the model.The SoftMax function was introduced to convert fitness values,dynamically adjusting crossover and mutation probabilities.A similarity-based crossover judgment method was employed to reduce ineffective crossovers.A catastrophe strategy was included to prevent premature convergence,and the warehouse layout was updated in real-time to adapt to the changing product demand in each cycle.Finally,the simulation experiments of different scales were compared for verification.The results show that the proposed algorithm can significantly improve solution efficiency while maintaining solution quality.With the increase of order volume,the optimization effect of the proposed strategy becomes more pronounced,with high-turnover pods having a greater probability of being located closer to picking stations.Compared to static fixed allocation and dynamic nearest allocation strategies,the proposed strategy can reduce the total AGV picking paths by 42%and 21%respectively in large-scale experiments,significantly enhancing the system's picking efficiency.The research results can provide a reference for further optimizing the pod location assignment in RMFS.关键词
移动机器人履行系统/自动导引车/动态货位分配/自适应遗传算法/贪心算法Key words
robotic mobile fulfillment systems/automated guided vehicles/dynamic pod location assignment/adaptive genetic algorithm/greedy algorithms分类
信息技术与安全科学引用本文复制引用
田帅辉,林诗阳..基于贪心-遗传混合算法的RMFS动态货位分配优化研究[J].铁道科学与工程学报,2025,22(5):2089-2099,11.基金项目
重庆市教育委员会人文社会科学研究项目(22SKGH127,23SKGH420) （22SKGH127,23SKGH420）
重庆市社会科学规划项目(2023NDYB79) （2023NDYB79）
