订单拣选效率：自动化药品分发系统中的分散存储与聚类分配策略
Order picking efficiency: A scattered storage and clustered allocation strategy in automated drug dispensing systems
AI总结：
本文针对智慧医院门诊药房自动化药品分发系统（ADDSs）的订单拣选效率问题，提出两阶段分散存储与聚类分配（SSCA）策略优化存储位置分配，并开发双目标整数规划模型，显著提升了人机协作环境下的拣选效率。
摘要：
在智慧医院中，优化门诊药房的处方订单履行流程至关重要。一种极具前景的设备——自动化药品分发系统（ADDSs）应运而生，以简化这些流程。这些系统涉及由ADDSs辅助的人工订单拣选员。ADDS的机械臂将药箱从存储位置运送到输入/输出（I/O）点，而药剂师在I/O点从药箱中分拣所需药品。本文重点协调ADDS和药剂师，以优化订单拣选策略。订单拣选系统的另一个关键方面是存储位置分配问题（SLAP），它决定了药品到存储位置的分配。在本研究中，我们将ADDS视为一个智能仓库，并提出了一种两阶段的分散存储与聚类分配（SSCA）策略，以优化ADDSs的SLAP。第一阶段主要采用分散存储方法，我们开发了一个数学规划模型来相应地对药品进行分组。在第二阶段，我们引入了一种顺序交替（SA）启发式算法，该算法考虑了药品需求频率和药品之间的相关性，以有效地对药品进行聚类和定位。为了评估所提出的SSCA策略，我们针对ADDSs中的订单拣选问题开发了一个双目标整数规划模型，以在保持订单最短平均拣选时间的同时，最小化处方订单中访问的机器数量。数值结果表明，所提出的策略能够优化ADDSs中的SLAP，并在人机协作环境下显著提高ADDSs的订单拣选效率。
英文摘要：
In the smart hospital, optimizing prescription order fulfilment processes in outpatient pharmacies is crucial. A promising device, automated drug dispensing systems (ADDSs), has emerged to streamline these processes. These systems involve human order pickers who are assisted by ADDSs. The ADDS's robotic arm transports bins from storage locations to the input/output (I/O) points, while the pharmacist sorts the requested drugs from the bins at the I/O points. This paper focuses on coordinating the ADDS and the pharmacists to optimize the order-picking strategy. Another critical aspect of order-picking systems is the storage location assignment problem (SLAP), which determines the allocation of drugs to storage locations. In this study, we consider the ADDS as a smart warehouse and propose a two-stage scattered storage and clustered allocation (SSCA) strategy to optimize the SLAP for ADDSs. The first stage primarily adopts a scattered storage approach, and we develop a mathematical programming model to group drugs accordingly. In the second stage, we introduce a sequential alternating (SA) heuristic algorithm that takes into account the drug demand frequency and the correlation between drugs to cluster and locate them effectively. To evaluate the proposed SSCA strategy, we develop a double objective integer programming model for the order-picking problem in ADDSs to minimize the number of machines visited in prescription orders while maintaining the shortest average picking time of orders. The numerical results demonstrate that the proposed strategy can optimize the SLAP in ADDSs and improve significantly the order-picking efficiency of ADDSs in a human-robot cooperation environment.
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