Abstract: The automated vehicle storage and retrieval system is an emerging automated logistics technology in recent years,relying on the flexible configuration of Automated Guided Vehicles (AGV) and elevators,and possessing high throughput capacity and flexibility.With the increasing demand for logistics,minimizing system investment and energy consumption costs while maintaining efficient operation has become a pressing issue.An optimization goal to minimize investment costs and average task energy consumption costs was proposed,and a stochastic nonlinear integer programming model with dual constraints of average task cycle time and average idle AGV quantity was established.The interaction between energy consumption and charging during AGV operation was considered,and the system performance indicators were solved approximatively through a queuing network model.To further optimize system configuration,the approximate solution algorithm of the queuing network model was embedded into a heuristic algorithm to improve solution efficiency and quality.Case study results validated the effectiveness and accuracy of the proposed method,and sensitivity analysis provided valuable references for the parameter optimization of AGVs and elevators,further verifying the applicability of the method.

Key words: autonomous vehicle storage and retrieval system, multi-level rack, automatic guided vehicle charging, queuing network model, heuristic algorithms

摘要： 自动小车存取系统是近年来新兴的自动化物流技术,依托自动导引小车(AGV)和升降机的灵活配置,该系统具备较高的吞吐能力与柔性。随着物流需求的不断增长,如何在保持高效运作的同时,最小化系统的投资和能耗成本,成为亟待解决的问题。因此,提出最小化投资成本和平均任务能耗成本的优化目标,构建了以平均任务周期时间和平均空闲AGV数量为双重约束的随机非线性整数规划模型。模型考虑了AGV运行过程中能耗与充电情况的相互影响,并通过排队网模型来近似求解分析系统性能指标。同时为了进一步优化系统配置,将排队网模型的近似求解算法嵌入到启发式算法中进行求解,提升求解效率和解的质量。算例结果验证了所提方法的有效性和准确性,此外,敏感性分析结果为AGV和升降机的参数优化提供了有价值的参考,进一步验证了所提方法的适用性。

关键词: 自动小车存取系统, 多层级货架, AGV充电, 排队网络模型, 启发式算法