Scheduling of autonomous mobile robots with conflict-free routes utilising contextual-bandit-based local search

Jun, Sungbum; Choi, Chul Hun; Lee, Seokcheon

doi:10.1080/00207543.2022.2063085

Cited by 4 publications

(1 citation statement)

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“…The problem was partitioned into regions and routing sub-problems, which were solved by a multi-objective genetic algorithm and reinforcement learning approach. Jun et al [ 5 ] also studied the AMR scheduling problem in the deterministic environment and introduced an innovative local search algorithm to solve it.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic scheduling of autonomous mobile robots at hospitals

Cheng,

Zhao,

Yuan

et al. 2023

PLoS ONE

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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.

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Section: Literature Reviewmentioning

confidence: 99%