Evidence map›Paper›PMID 40594346›Full record

ArticleScientific reports2025

Efficient design of automated guided vehicle systems in operating theatres via discrete events simulation.

Yao Ge, Marcus Eng Hock Ong, Sean Shao Wei Lam

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yao GeHealth Service Research Centre, Singapore Health Services, Singapore, Singapore.
Marcus Eng Hock OngHealth Service Research Centre, Singapore Health Services, Singapore, Singapore.
Sean Shao Wei LamHealth Service Research Centre, Singapore Health Services, Singapore, Singapore. gmslasws@nus.edu.sg.

Funding

Singapore Health Assistive & Robotic Programme 192 22 00005
6 · The paper itself

Abstract

The integration of Automated Guided Vehicles (AGVs) into healthcare facilities has demonstrated significant benefits, particularly in environments with high internal transportation volumes. Research indicates the implementation of AGVs in hospital settings could save medical staff approximately 23% of their daily time, which could then be redirected towards direct patient care. This study aims to develop a framework for the efficient design of AGV systems deployed in existing operating theatres (OTs). A discrete events simulation (DES) model was developed for policy and operational challenges to AGV deployment in an existing OT complex. The DES model facilitates the evaluation of various AGV system configurations, which include different surgical instrument assortment algorithms, fleet sizes, and restocking frequencies. To determine the most efficient design of the AGV system in OTs, relative efficiency across design alternatives were evaluated with the super-efficiency data envelopment analysis (S-DEA). This approach yielded a composite efficiency score that reflects multiple, often conflicting, design objectives. Our findings suggest efficiency gains are notably substantial in scenarios of higher demand, particularly when an enhanced instrument assortment algorithm is employed alongside an increased number of AGVs. The DES model provided practical insights and recommendations for the design and deployment of AGV systems in OTs.

Indexed as

Operating RoomsAlgorithmsAutomationComputer SimulationHumansAutomated guided vehiclesData envelopment analysisDiscrete events simulationHealthcare logisticsLoad selectionSuper-efficiency

Identifiers

PMID40594346
PMCPMC12216486

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.