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考虑多车配合的autostore系统订单拣选排序研究
首发时间：2023-10-26
摘要：autostore系统是一种高密度智能仓储系统，储存性能优于同等规模传统仓库40%以上。现阶段对该系统中订单排序问题研究较少，且都严格要求单辆小车参与整套拣货动作，导致对相同料箱的多次翻拣，增加大量重复成本。为解决该问题，创新性地提出堆垛检索概念并设计多车协同拣货作业流程，以减少对料箱的重复翻箱。基于该作业方式建立订单排序优化模型用于求解最优订单序列，并进一步设计了启发式算法sa用于大规模算例求解。数值实验显示，在小规模问题中，sa算法与数学模型最优解的误差仅为0.95%到3.5%；在大规模问题中，sa的性能高出现有方法15%至72%。结果证明了该种作业流程的有效性及sa算法求解订单排序问题的优越性。
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research on order picking sorting in the autostore system with multi-vehicle collaboration
abstract：the autostore system is a high-density intelligent warehousing system, with a storage performance exceeding that of traditional warehouses of equivalent size by more than 40%. currently, there is limited research on the order sequencing problem within this system, and the existing studies strictly require the involvement of a single vehicle in the entire picking process. this leads to multiple retrievals of the same bin, resulting in a significant increase in redundant costs. to address this issue, we introduce the innovative concept of "stacked retrieval" and devise a multi-vehicle collaborative picking process to reduce the repeated handling of bins. based on this operational approach, we establish an order sequencing optimization model to determine the optimal order sequence. additionally, we design a heuristic algorithm named sa to address large-scale instances of the problem. numerical experiments reveal that, in small-scale scenarios, the sa algorithm yields an error ranging from 0.95% to 3.5% compared to the mathematical model\'s optimal solution. in large-scale scenarios, sa outperforms existing methods by 15% to 72%. these results convincingly demonstrate the effectiveness of this operational approach and the superior performance of the sa algorithm in solving the order sequencing problem.
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考虑多车配合的autostore系统订单拣选排序研究
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