This study focuses on the replenishment process of the robotic dispensing system (RDS) in a central fill pharmacy (CFP). The RDS is capable of autonomously counting and filling tens of thousands of prescription orders each day while being replenished by operators. If the replenishment is not completed on time and the dispenser becomes empty while orders continue to arrive, the RDS will experience a problem called a rundry error and cannot fill orders until the replenishment is complete. Because rundry errors significantly degrade the performance of CFPs, there is an urgent need to analyze and understand the replenishment process of the RDS to prevent these errors. The main objective of this study is to develop a systematic approach to model the stochastic behavior of the replenishment process by using a continuous-time Markov Chain and to find the optimal reorder point (ROP), canister size, and the number of operators that minimize the replenishment costs. Numerical experiment results show that ROP, canister size, and the number of operators have a significant effect on the performance of the RDS. In the dispenser analyzed in this study, increasing the ROP from 0.5 to 0.5 led to a 26.7% reduction in downtime and a 49.2% reduction in total costs. Similarly, Increasing the canister size from a 0.5-L canister to a 2-L canister led to a 10.5% reduction in downtime and a 69.5% reduction in total costs. The results show that the proposed approach can be used to optimize the replenishment process to minimize cost.

这项研究的重点是集中填充药房（CFP）中机器人分配系统（RDS）的补货过程。RDS每天可以自动计算和填写成千上万的处方订单，同时可以由操作员补充。如果补货没有按时完成，并且在订单继续到达时分配器变空，则RDS会遇到一个问题，称为运行失误，直到补货完成后才可以补货。由于运行失误会严重降低CFP的性能，因此迫切需要分析和理解RDS的补货过程以防止这些错误。这项研究的主要目的是开发一种系统方法，通过使用连续时间马尔可夫链对补货过程的随机行为进行建模，并找到最优的再订货点（ROP），罐的大小以及最小化操作员的数量。补货费用。数值实验结果表明，ROP，碳罐尺寸和操作员数量对RDS的性能有重要影响。在本研究中分析的分配器中，ROP从0.5增加到0.5导致停机时间减少了26.7％，总成本减少了49.2％。同样，将容器的大小从0.5 L的容器增加到2 L的容器可导致停机时间减少10.5％，总成本减少69.5％。结果表明，所提出的方法可用于优化补货过程以最小化成本。