  This paper studies the scheduling of autonomous mobile robots (AMRs) at
hospitals where the stochastic travel times and service times of AMRs are
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, some properties are identified and
incorporated into an improved tabu search (I-TS) algorithm for problem-solving.
Experimental evaluations demonstrate that the I-TS algorithm outperforms
existing methods by producing high-quality solutions. Based on the
characteristics of healthcare requests and the AMR working environment,
scheduling AMRs reasonably can effectively provide medical services, improve
the utilization of medical resources, and reduce hospital costs.
