A discrete event simulation evaluation of distributed operating room scheduling

Wang, Shuo; Roshanaei, Vahid; Aleman, Dionne M.; Urbach, David R.

doi:10.1080/19488300.2016.1226994

Cited by 41 publications

(21 citation statements)

References 35 publications

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“…The signals in the network could be relayed via these VCs [16][17][18][19][20][21]. A number of VCs can communicate smoothly with central ports with any downlink [21][22][23][24][25][26] or uplink [27][28][29][30][31][32][33] transmission of data.…”

Section: System Modelmentioning

confidence: 99%

“…To establish the complete, transmit power following the multi-hop maximum ratio combining (MHMRC) [29][30][31][32][33] route, an n-hop connection from the wireless terminal to the central port is investigated, where port #i = 0 is the mobile terminal, port #i = n is the central port, and port #i = 1~n − 1 is the intermediate port. The received power P r (1) from the mobile terminal #i = 0 is given by…”

Section: System Modelmentioning

confidence: 99%

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A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks

Wang

Gillbanks

et al. 2019

Symmetry

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With the development of modern society, there are not only many voice calls being made over wireless communication systems, but there is also a great deal of demand for data services. There are increasing demands from the general public for more information data, especially for high-speed services with elevated Gbps levels. As is well known, higher sending power is needed once data rates increase. In order to solve this problem, virtual cellular networks (VCNs) can be employed in order to reduce these peak power shifts. If a VCN works well, mobile ports will receive their own wireless signals via individual cells, and then, the signals will access core networks with the help of a central terminal. Power control can improve the power capacity in multi-hop networks. However, the use of power control will also have a negative impact on network connectivity, delay, and capacity. In order to address the problem, this paper compares specific control methods and capacities in multi-hop networks. Distributed chicken game algorithm power control (DCGAPC) methods are presented in order to reach acceptable minimum levels of network delay and maximum network capacity and connectivity. Finally, a computer simulation is implemented, and the results are shown.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks

Wang

Gillbanks

et al. 2019

Symmetry

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“…The allocation plan is then evaluated and fine-tuned by simulation. Wang et al (2016) and Baesler et al (2015) also use discrete event simulation and optimisation to schedule ORs. Lamiri et al (2009) instead propose several heuristics to solve the stochastic surgery planning problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Planning and scheduling operating rooms for elective and emergency surgeries with uncertain duration

Kroer

Foverskov

Vilhelmsen

et al. 2018

Operations Research for Health Care

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“…There are various methods used to schedule individuals in hospitals or clinics using optimization which is a growing concern in the areas of Operations Research and supply chain logistics (e.g., Ahmadi-Javid, Jalali, &Klassen, 2017 andWang, Roshanaei, Aleman, Urbach, 2016). Many types of methods have been applied ranging from linear programming to stochastic and dynamic programming.…”

Section: Introductionmentioning

confidence: 99%

Practical Optimal Scheduling for Surgery

Allen

Hernandez

Roychowdhury

et al. 2020

Proceedings of the International Symposium on Human Factors and

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There is a great need for creating schedules that are optimized. Yet, some individuals have had less than desirable experiences with “optimal” scheduling. This could have been due to prioritization of the wrong criteria, leading to schedules that did not make practical sense, or that were math-intensive and were not able to be easily interpreted. Also, there are many types of optimization problem formulations and solution methods. Here, we divide the formulations into two major types: batched and online scheduling classes are discussed. A different technique has been created that allows schedules to be made that are not only optimal, based on the formulations or framing, but that are actually useful. Here, we discuss two types of methods, one batched called Genetic Algorithms with an Earliest Due Date encoding Method (GAGEDD) and the other online called Markov Decision Processes and Reinforcement Learning extensions. These methods are already being employed to create practical and optimal schedules that can include many different constraints and are able to instantly take into account new scheduling requests and take optimal actions regardless of what state the system is currently in. Especially with current world events (COVID-19), it is important to intelligently schedule patients.

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A discrete event simulation evaluation of distributed operating room scheduling

Cited by 41 publications

References 35 publications

A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks

A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks

Planning and scheduling operating rooms for elective and emergency surgeries with uncertain duration

Practical Optimal Scheduling for Surgery

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