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. We formulate a stochastic mixed integer programming model 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, we identify several properties for generating high-quality solutions efficiently. We improve the Variable Neighborhood Search (VNS) algorithm by incorporating the properties of the AMR scheduling problem to solve the model. Experimental results demonstrate that VNS generates higher-quality solutions compared to existing methods. By intelligently arranging the driving routes of AMRs for both charging and service requests, we achieve substantial cost reductions for hospitals, enhancing the utilization of medical resources.

翻译：自主移动机器人（AMR）在医院运输和服务任务中发挥着关键作用，有助于提升效率并满足医疗需求。本文研究了智能医院中AMR调度策略的优化问题，其中AMR的服务时间和行驶时间具有随机性。我们构建了一个随机混合整数规划模型，通过减少AMR数量及行驶距离来最小化医院总成本，同时满足AMR电池荷电状态、容量以及医疗请求时间窗等约束。为了实现这一目标，我们识别了若干性质，用于高效生成高质量解。通过融入AMR调度问题的性质，改进了可变邻域搜索（VNS）算法以求解该模型。实验结果表明，与现有方法相比，VNS能生成更高质量的解。通过智能规划AMR在充电和服务请求中的行驶路线，我们为医院实现了显著的成本降低，从而提升了医疗资源的利用率。