@article {Toub20221,
	title = {Operating room scheduling 2019 survey},
	journal = {International Journal of Medical Engineering and Informatics},
	volume = {14},
	number = {1},
	year = {2022},
	note = {cited By 2},
	pages = {1-30},
	abstract = {Numerous optimisation problems in healthcare have been approached by researchers over the last three to four decades. Hospital logistics - organised and structured to secure patient satisfaction in terms of quality, quantity, time, security and least cost - forms part of the quest for global performance. We provide herein a review of recent study and applications of operations research in healthcare. In particular, we survey work on optimisation problems, focusing on the planning and scheduling of operating rooms. The latter is a highly strategic place within the hospital as it requires key medical competence and according to Macario (2008) surgical sector expenditure represents nearly a third of a hospital{\textquoteright}s budget. We analyse recent research on operating room planning and scheduling from 2008 to 2019; our evaluation is based on patient characteristics, performance measurement, the solution techniques used in the research and the applicability of the research to real life cases. The searches were based on PubMed, Web of Science, Science Direct and Google Scholar databases. Copyright {\textcopyright} 2022 Inderscience Enterprises Ltd.},
	keywords = {adult, agricultural worker, Article, budget, clinical evaluation, computer assisted tomography, Computer simulation, cost effectiveness analysis, eutrophication, febrile neutropenia, female, genetic algorithm, health care cost, health care facility, health care system, hip replacement, hospital cost, hospitalization, human, intensive care unit, length of stay, Machine learning, male, mathematical model, operating room personnel, operation duration, Patient satisfaction, population size, stochastic model, system analysis, Time series analysis, total quality management, vaccination, work environment, workload},
	doi = {10.1504/IJMEI.2022.119307},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120773204\&doi=10.1504\%2fIJMEI.2022.119307\&partnerID=40\&md5=a2ac2109f88463231d4e37b0a988843f},
	author = {Toub, M. and Souissi, O. and Achchab, S.}
}
@conference {Maha2020,
	title = {The Two Phases Method for operating rooms planning and scheduling},
	booktitle = {2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020},
	year = {2020},
	note = {cited By 2},
	abstract = {Over the last three or four decades, there have been numerous optimization problems in Healthcare which have been approached by researchers. Hospital logistics which must be organized and structured in order to secure patient satisfaction in terms of quality, quantity, time, security and least cost, forms part of the quest for global performance.According to the literature review, the problem of operating rooms planning and scheduling involves different conflicting objectives while considering constraints on availability of rooms, patients and doctors. In this paper, we proposed the Two Phases Method (TPM), which is a general technique that is likely to solve multi-objective combinatorial optimization (MOCO) problems.As it is known, TPM has never been applied to solve operating room planning and scheduling problem. In this paper, we developed the TPM to resolve the cited issue, while focusing on optimizing both total completion time and patients{\textquoteright} waiting time. {\textcopyright} 2020 IEEE.},
	keywords = {Combinatorial Optimization, Conflicting objectives, Constraint satisfaction problems, Decision making, Hospital logistics, Industrial management, Literature reviews, Multiobjective combinatorial optimization, Operating rooms, Optimization problems, Patient satisfaction, Planning and scheduling, Scheduling, Total completion time},
	doi = {10.1109/ICTMOD49425.2020.9380584},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103741668\&doi=10.1109\%2fICTMOD49425.2020.9380584\&partnerID=40\&md5=a5086170077f1fe352eb34de4e820aac},
	author = {Maha, T. and Achchab, S. and Omar, S.}
}
