Physical design optimization for automated drug dispensing systems in a human-machine interaction environment

Automated drug dispensing systems (ADDSs) are increasingly in demand in today's pharmacies due to the growing aging population. Recognizing the practical needs faced by hospitals utilizing ADDSs, this study focuses on optimizing the physical design of ADDSs in a human-machine interaction environment. Specifically, we investigate the retrieval sequencing of drugs among successive prescription orders. To compare the efficiency of ADDSs with the different number of input/output designs, we formulate dual command retrieval sequencing models that optimize the retrieval sequence of drugs in adjacent prescription orders. In particular, we consider the stochastic service time of pharmacists in the 0-1 integer programming models to analyze the impact on humans. Through experimental comparisons of average picking times for prescription orders under different layout designs, the system layout with two input/output points significantly enhances the efficiency of prescription order fulfillment within a human-machine interaction environment. Furthermore, the proposed retrieval sequence method outperforms dynamic programming, greedy, and random strategies in improving prescription order-picking efficiency.

A Quick Response Algorithm for Dynamic Autonomous Mobile Robot Routing Problem with Time Windows

This paper investigates the optimization problem of scheduling autonomous mobile robots (AMRs) in hospital settings, considering dynamic requests with different priorities. The primary objective is to minimize the daily service cost by dynamically planning routes for the limited number of available AMRs. The total cost consists of AMR's purchase cost, transportation cost, delay penalty cost, and loss of denial of service. To address this problem, we have established a two-stage mathematical programming model. In the first stage, a tabu search algorithm is employed to plan prior routes for all known medical requests. The second stage involves planning for real-time received dynamic requests using the efficient insertion algorithm with decision rules, which enables quick response based on the time window and demand constraints of the dynamic requests. One of the main contributions of this study is to make resource allocation decisions based on the present number of service AMRs for dynamic requests with different priorities. Computational experiments using Lackner instances demonstrate the efficient insertion algorithm with decision rules is very fast and robust in solving the dynamic AMR routing problem with time windows and request priority. Additionally, we provide managerial insights concerning the AMR's safety stock settings, which can aid in decision-making processes.