Stochastic scheduling of autonomous mobile robots at hospitals

TL;DR

This paper presents an improved tabu search algorithm for stochastic scheduling of autonomous mobile robots in hospitals, aiming to minimize costs and improve resource utilization despite environmental uncertainties.

Contribution

It introduces an enhanced tabu search method tailored for stochastic AMR scheduling in hospitals, incorporating problem-specific properties for better solutions.

Findings

The I-TS algorithm outperforms existing methods in solution quality.

Scheduling AMRs effectively reduces hospital costs.

Improved scheduling enhances medical service delivery and resource utilization.

Abstract

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.