Application of Operations Research Methods in Operating Room Scheduling - a Short Survey
2024 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, CCECE 2024(2024)
St Marys Univ
Abstract
The surgical services provided in the operating rooms of the hospital are an essential part of the healthcare system. These services are usually life-threatening and time-sensitive, and require highly trained surgeons and staff, as well as the latest medical equipment. Furthermore, because of the high cost of these resources, hospitals can have only a limited number of operating rooms and staff members. Thus, it is crucial to optimize various aspects of operating room functions to maximize overall utilization.This survey summarizes various optimization models proposed in the literature for such problems faced in operating rooms. The goal of this work is to provide researchers with a guide for further research in the field. Methods: This survey includes articles from Pubmed, since 2010. The search queries were related to the terms scheduling operating room, optimization model, and queuing. More than 400 articles were found, and the authors filtered the articles based on their relevance to this survey.The analysis found that a) the studies are usually very specific to optimizing a particular problem related to the operating room, b) datasets are not available in the literature and it is difficult to conduct comparative analysis, similarly c) the source code is not available on publicly available repositories like GitHub, and d) it is difficult to replicate the studies and establish benchmarks.
MoreTranslated text
Key words
Optimization,Operating room scheduling,Mixed Integer Linear Programming,Queuing,Nonlinear Programming,Dynamic Programming
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined