Stochastic scheduling of autonomous mobile robots at hospitals

Abstract

The outbreak of the New Coronavirus has significantly increased the vulnerability of medical staff. This paper addresses the safety and stress relief of medical personnel by proposing a solution to the scheduling problem of autonomous mobile robots (AMRs) in a stochastic environment. Considering the stochastic nature of travel and service times for AMRs affected by the surrounding environment, the routes of AMRs are planned to minimize the daily cost of the hospital (including the AMR fixed cost, penalty cost of violating the time window, and transportation cost). To efficiently generate high-quality solutions, we identify several properties and incorporate them into an improved Tabu Search (I-TS) algorithm for problem-solving. Experimental evaluations demonstrate that the I-TS algorithm outperforms existing methods by producing higher-quality solutions. By leveraging the characteristics of medical request environments, we intelligently allocate an appropriate number of AMRs to efficiently provide services, resulting in substantial cost reductions for hospitals and enhanced utilization of medical resources. These findings confirm the effectiveness of the proposed stochastic programming model in determining the optimal number of AMRs and their corresponding service routes across various environmental settings