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Autonomous mobile robots (AMRs) play a crucial role in transportation andservice tasks at hospitals, contributing to enhanced efficiency and meetingmedical demands. This paper investigates the optimization problem of schedulingstrategies for AMRs at smart hospitals, where the service and travel times ofAMRs are stochastic. A stochastic mixed-integer programming model is formulatedto minimize the total cost of the hospital by reducing the number of AMRs andtravel distance while satisfying constraints such as AMR battery state ofcharge, AMR capacity, and time windows for medical requests. To address thisobjective, some properties of the solutions with time window constraints areidentified. The variable neighborhood search (VNS) algorithm is adjusted byincorporating 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 achievedthrough intelligently arranging the driving routes of AMRs for both chargingand service requests, resulting in substantial cost reductions for hospitalsand enhanced utilization of medical resources.

The Multi-Trip Autonomous Mobile Robot Scheduling Problem with Time Windows in a Stochastic Environment at Smart Hospitals