Order picking efficiency: A scattered storage and clustered allocation strategy in automated drug dispensing systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909105294147584 |
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| author | Yuan, Mengge Zhao, Ning Wu, Kan Cheng, Lulu |
| author_facet | Yuan, Mengge Zhao, Ning Wu, Kan Cheng, Lulu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08683 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Order picking efficiency: A scattered storage and clustered allocation strategy in automated drug dispensing systems Yuan, Mengge Zhao, Ning Wu, Kan Cheng, Lulu Applications Optimization and Control 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. |
| title | Order picking efficiency: A scattered storage and clustered allocation strategy in automated drug dispensing systems |
| topic | Applications Optimization and Control |
| url | https://arxiv.org/abs/2402.08683 |