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

TL;DR

This paper develops a stochastic mixed-integer programming model and a variable neighborhood search algorithm to optimize the scheduling of autonomous mobile robots in smart hospitals, reducing costs and improving resource utilization.

Contribution

It introduces a novel stochastic scheduling model with properties tailored for hospital AMRs and adapts VNS to efficiently solve it, enhancing hospital operational efficiency.

Findings

VNS achieves high-quality solutions for AMR scheduling.

Optimized routes reduce hospital costs and improve resource utilization.

The approach effectively handles stochastic service and travel times.

Abstract

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. A stochastic mixed-integer programming model is formulated 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, some properties of the solutions with time window constraints are identified. The variable neighborhood search (VNS) algorithm is adjusted by incorporating 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 achieved through intelligently arranging the driving routes of AMRs for both charging and service requests, resulting in substantial cost reductions for hospitals and enhanced utilization of medical resources.