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Medication planogram design to minimize collation delays and makespan in parallel pharmaceutical automatic dispensing machines

机译:药物普通图设计,以最大限度地减少并行制药自动分配机中的整理延迟和MakEspan

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This paper presents an optimization study of the medication planogram design of parallel pharmaceutical automatic dispensing (PPAD) machines. The PPAD machines are widely used in central fill pharmacies to improve the prescription dispensing speed and accuracy nowadays. The objectives of this medication planogram design are to minimize the collation delay and makespan, which are evaluated along with the prescription scheduling problem. The planogram design has two levels: (1) the medication-dispenser assignment within the machine, which determines the dispensing speed and (2) the medication-machine assignment, which affects the collation delay (CD) for prescription orders. The integrated and hierarchical approaches are proposed to optimize the short-term and long-term medication planogram design, respectively. For the short-term planogram design, an integrated mathematical model and a two-link non-dominated genetic algorithm II (TL-NSGA-II) are proposed to solve the planogram design and order scheduling problems simultaneously. For the long-term planogram design, the planogram design is optimized based on the given support of medications sets, and then a tabu search non-dominated genetic algorithm II (TS-NSGA-II) is applied to solve this problem hierarchically. Thirty random samples are generated to test the proposed algorithms’ efficiency. Compared to the industrial package (the longest processing time heuristic), the proposed approaches can reduce collation delays and makespan significantly. The short-term planogram design, which optimizes the planogram design and order scheduling problems simultaneously, is more efficient if the planogram reset cost is trivial, compared to the long-term planogram design.
机译:本文介绍了并行药品自动分配(PPAD)机器的药物普通图设计的优化研究。 PPAD机器广泛用于中央填充药店,以提高处方分配速度和准确性。这种药物普通图设计的目的是最大限度地减少融合延迟和MakEspan,其与处方调度问题一起评估。楷模图设计有两个水平:(1)机器内的药物分配器分配,它决定了药物机构的分配速度和(2),影响处方订单的核对延迟(CD)。提出了集成的和分层方法,分别优化短期和长期药物普通图设计。对于短期地图设计,提出了一种集成的数学模型和双链路非主导遗传算法II(TL-NSGA-II),以解决平面图设计和同时订购安排问题。对于长期的平面图设计,基于对药物组的给定支持进行了优化的平面图设计,然后应用了禁忌搜索非主导的遗传算法II(TS-NSGA-II)来分层解决这个问题。生成三十个随机样本以测试所提出的算法的效率。与工业包(最长的加工时间启发式)相比,所提出的方法可以显着减少融合延迟和MEPESPAN。与长期的平面图设计相比,在同时优化平面图设计和顺序调度问题的短期平面图设计,更有效率,更有效率。

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