Order picking efficiency: a scattered storage and clustered allocation strategy in automated drug dispensing systems

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 robotic arm of the ADDS 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 optimizing the drug storage location assignment policy (SLAP) and order-picking strategy in ADDSs under a human-robot cooperation environment. 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 uniquely integrates dynamic balancing of drug demand frequency and pairwise correlation. To address the order-picking problem in ADDSs, we develop a double objective integer programming model that minimizes 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.