多层穿梭车系统换层策略分析与效率优化

Efficiency Analysis and Optimization of Layer-Switching Strategies for Automated Vehicle Storage and Retrieval Systems

摘要

为研究漫游者模式多层穿梭车系统中穿梭车性能及其换层策略对系统效率的影响，构建了基于离散事件仿真方法的参数化、模块化的多层穿梭车系统效率评价模型。通过控制变量法，效率评价模型分析了穿梭车数量、穿梭车速度、货架布局等因素对系统效率的影响，并研究了 4 种换层策略对系统效率的影响。“穿梭车数量递增”仿真实验结果表明，货物提升机是制约系统效率的关键，配置过多的穿梭车会降低每台穿梭车的利用率；相比于巡回者模式，漫游者模式多层穿梭车系统能更有效平衡穿梭车与提升机之间的搬运能力，提升穿梭车利用率。“穿梭车换层策略”对比实验结果表明，相比于工程实践中常用的“按时间先后顺序”的换层策略，“按起点距离远近”的换层策略更优，可将系统效率提升 3.1% ~ 5.8%。

Abstract

To investigate the impact of shuttle performance and layer-switching strategies on the efficiency of the tier-to-tier automated vehicle storage and retrieval system (TTAVS/RS), a parameterized and modular efficiency evaluation model is developed based on discrete event simulation. Using the control variable approach, the model analyzes the effects of shuttle quantity, shuttle speed, and rack layout on system efficiency, and further examines the influence of four layer-switching strategies. Simulation experiments with increasing shuttle quantities reveal that the goods lift is the primary bottleneck constraining system efficiency, and that excessive shuttles reduce per-shuttle utilization; compared with the tier-captive mode, the tier-to-tier mode more effectively balances the handling capacity between shuttles and lifts, thereby enhancing shuttle utilization. Comparative experiments on layer-switching strategies demonstrate that the "shortest origin distance first" strategy outperforms the commonly adopted "first-come-first-served" strategy in engineering practice improving system efficiency by 3.1%–5.8%.

关键词

漫游者模式 / 多层穿梭车系统 / 穿梭车换层策略 / 离散事件仿真 / 效率分析与优化

Key words

tier-to-tier mode / automated vehicle storage and retrieval system / layer-switching strategy / discrete event simulation / efficiency analysis and optimization

参考文献

[1] Ekren B Y, Heragu S S, Krishnamurthy A, et al. Simulation based experimental design to identify factors affecting performance of AVS/RS[J]. Computers & Industrial Engineering, 2010, 58(1): 175-185.

[2] Zou B P, Xu X H, Gong Y M, et al. Modeling parallel movement of lifts and vehicles in tier-captive vehicle-based warehousing systems[J]. European Journal of Operational Research, 2016, 254(1): 51-67.

[3] Yang J J, Guo X L, Chen R, et al. Retrieval request sequencing in a novel tier-captive AVS/RS with a double-capacity lift[J]. Transportation Research Part E: Logistics and Transportation Review, 2025, 194: 103955.

[4] Zhao X F, Zhang R X, Zhang N, et al. Analysis of the shuttle-based storage and retrieval system[J]. IEEE Access, 2020, 8: 146154-146165.

[5] Arslan B, Ekren B Y. Transaction selection policy in tier-to-tier SBSRS by using deep Q-learning[J]. International Journal of Production Research, 2023, 61(21): 7353-7366.

[6] Dong W Q, Jin M Z. Travel time models for tier-to-tier SBS/RS with different storage assignment policies and shuttle dispatching rules[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 155: 102485.

[7] Lerher T, Ficko M, Palčič I. Throughput performance analysis of automated vehicle storage and retrieval systems with multiple-tier shuttle vehicles[J]. Applied Mathematical Modelling, 2021, 91: 1004-1022.

[8] Battarra I, Accorsi R, Manzini R, et al. Storage efficiency in a deep-lane AVS/RS[J]. IFAC-PapersOnLine, 2022, 55(10): 1337-1342.

[9] Eder M. Analytical examination of shuttle-based storage and retrieval systems with multiple-capacity lifts[J]. The International Journal of Advanced Manufacturing Technology, 2024, 133(9): 5053-5064.

[10] Eder M. An analytical approach for a performance calculation of shuttle-based storage and retrieval systems with multiple-deep and class-based storage[J]. Production & Manufacturing Research, 2022, 10(1): 321-336.

[11] Kosanić N, Marolt J, Zrnić N, et al. Travel time model for multiple-deep shuttle-based storage and retrieval systems[J]. International Journal of Production Research, 2024, 62(7): 2606-2639.

[12] Zhao N, Luo L, Lodewijks G. Scheduling two lifts on a common rail considering acceleration and deceleration in a shuttle based storage and retrieval system[J]. Computers & Industrial Engineering, 2018, 124: 48-57.

[13] Chen R, Yang J J, Yu Y G, et al. Retrieval request scheduling in a shuttle-based storage and retrieval system with two lifts[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 174: 103117.

[14] Chen R, De Koster R, Yu Y G, et al. Blockage-free storage assignment and storage/retrieval scheduling in autonomous vehicle storage and retrieval systems[J]. Naval Research Logistics, 2024, 71(4): 575-594.

[15] 许丽丽, 詹燕, 鲁建厦, 等 . 四向穿梭车仓储系统复合作业调度优化[J]. 浙江大学学报: 工学版, 2023, 57(11): 2188-2199.

[16] Xu Lili, Zhan Yan, Lu Jiansha, et al. Compound operation scheduling optimization in four-way shuttle warehouse system[J]. Journal of Zhejiang University: Engineering Science, 2023, 57(11): 2188-2199.

[17] Lerher T, Ekren Y B, Sari Z. Simulation analysis of shuttle based storage and retrieval systems[J]. International Journal of Simulation Modelling, 2015: 48-59.

[18] 张经天, 马莹, 翁迅, 等 . 四向穿梭车系统的提升机调度优化[J]. 北京邮电大学学报, 2022, 45(1): 19-25.

[19] Zhang Jingtian, Ma Ying, Weng Xun, et al. Optimization of lifts scheduling for four-way shuttle systems[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(1): 19-25.

[20] 雷斌, 金彦彤, 刘海龙. 结合 FJSP 问题的跨层穿梭车仓储系统作业调度研究[J]. 计算机集成制造系统, 2023, 29(8): 2751-2760.

[21] Lei Bin, Jin Yantong, Liu Hailong. Job scheduling of cross layer shuttle-based storage system combined with FJSP problem[J]. Computer Integrated Manufacturing Systems, 2023, 29(8): 2751-2760.

基金资助

国家自然科学基金项目(52075036)

中国博士后科学基金项目(2025M770782)