Autonomous mobile robots (AMRs) play a crucial role in transportation and service tasks at hospitals, contributing to enhanced efficiency and meeting medical demands. This paper investigates the optimization problem of scheduling strategies for AMRs at smart hospitals, where the service and travel times of AMRs are stochastic. A stochastic mixed-integer programming model is formulated to minimize the total cost of the hospital by reducing the number of AMRs and travel distance while satisfying constraints such as AMR battery state of charge, AMR capacity, and time windows for medical requests. To address this objective, some properties of the solutions with time window constraints are identified. The variable neighborhood search (VNS) algorithm is adjusted by incorporating the properties of the AMR scheduling problem to solve the model. Experimental results demonstrate that VNS generates high-quality solutions. Both enhanced efficiency and the meeting of medical demands are achieved through intelligently arranging the driving routes of AMRs for both charging and service requests, resulting in substantial cost reductions for hospitals and enhanced utilization of medical resources.

翻译：自主移动机器人（AMR）在医院的运输和服务任务中发挥关键作用，有助于提升效率并满足医疗需求。本文研究智能医院中AMR调度策略的优化问题，其中AMR的服务时间与行驶时间具有随机性。构建了随机混合整数规划模型，在满足AMR电池荷电状态、容量及医疗请求时间窗等约束条件下，通过减少AMR数量与行驶距离来最小化医院总成本。为实现此目标，识别了具有时间窗约束的解决方案的若干性质。通过融入AMR调度问题的特性，对可变邻域搜索（VNS）算法进行改进以求解模型。实验结果表明，VNS算法能生成高质量解决方案。通过智能规划AMR面向充电请求与服务请求的行驶路线，既提升了运行效率又满足了医疗需求，从而为医院显著降低运营成本，并提高医疗资源利用率。