Stochastic programming analysis and solutions to schedule overcrowded operating rooms in China

Xiao, Guanlian; Jaarsveld, Willem van; Dong, Ming; Klundert, Joris van de

doi:10.1016/j.cor.2016.04.017

Cited by 31 publications

(12 citation statements)

References 27 publications

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“…In this technique, the main mathematical model is decomposed into two (or multiple) smaller formulations which are wellknown as master and sub-problems, and instead of solving the main model, these smaller models are solved in an iterative approach which is more efficient (see Birge and Louveaux (1997) for the stepwise and detailed descriptions). Five decades after the introduction of the method, the classic approach and its extensions have been widely applied in various applications including ORs management problems (Denton and Gupta, 2003;Denton, Viapiano, & Voglet, 2007Batun, Denton, Huschka, & Schaefer, 2011;Xiao, van Jaarsveld, Dong, & van de Klundert, 2016;Riise, Mannino, & Lamorgese, 2016;Roshanaei et al, 2017a,b;etc.). In this study, we apply a Benders' decomposition technique enriched with some valid inequalities, proposed in the literature to augment the relation between master and sub-problem and to improve the performance of the technique, as a solution approach.…”

Section: Solution Methods For the Stochastic/uncertain Mathematical Formulationsmentioning

confidence: 99%

“…It is worth noting that to elevate the performance of the customary and standard BD algorithm and to cover the drawback of poor approximation of , (especially early in the algorithm), we take benefit from valid inequalities stemming from Jensen inequality (Jensen, 1906). In this regard, we add set of constraints (D3)-(D6) which are based on the average value of the surgery duration as proposed in Batun et al (2011) and Xiao et al (2016) to the master problem.…”

Section: Tablementioning

confidence: 99%

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Uncertainty in advance scheduling problem in operating room planning

Kamran

Karimi

Dellaert

2018

Computers & Industrial Engineering

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Section: Solution Methods For the Stochastic/uncertain Mathematical Formulationsmentioning

confidence: 99%

Section: Tablementioning

confidence: 99%

Uncertainty in advance scheduling problem in operating room planning

Kamran

Karimi

Dellaert

2018

Computers & Industrial Engineering

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“…Although uncertainty optimizations can handle this type of problem, precise probability distributions of the uncertain parameters are required. [7][8][9][10] These are usually very time-consuming and expensive to obtain. The interval analysis method has been widely used to address parameter uncertainties in engineering.…”

Section: Introductionmentioning

confidence: 99%

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

Shi

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the traditional design of a vehicle suspension system, mechanical parameters, such as spring stiffness and tire radial stiffness, are determined before optimizing the coordinates of suspension hard-points based on handling and stability. However, the mechanical parameters of vehicle suspension systems vary with repeated changes in ambient temperature and loading conditions. Consequently, the original hard-point coordinates cannot guarantee satisfactory handling and stability after long-term vehicle use. To address this issue, an A0 class vehicle with a MacPherson suspension was modeled in ADAMS/Car and was validated against the results of field tests. The relationships between the maximum absolute values of front wheel alignment parameters during parallel wheel travel analysis and the coordinates of suspension hard-points, spring stiffness, and tire radial stiffness were determined using support vector regression. Next, a multi-objective optimization function was formulated based on the interval analysis method. Finally, a novel double-loop multi-objective particle swarm optimization algorithm was designed for global optimization of hard-point coordinates. The ADAMS simulation results indicated that, compared with the vehicle with the original hard-point coordinates, the double-loop multi-objective particle swarm optimization algorithm, the traditional multi-objective particle swarm optimization algorithm, and the genetic algorithm can effectively reduce the variation ranges of front wheel alignment parameters, regardless of the values of the mechanical parameters. It also showed that the double-loop multi-objective particle swarm optimization algorithm performs better than the traditional multi-objective particle swarm optimization and the genetic algorithm with both the original and changed mechanical parameters. Except for an increase in the variation range of the caster angle by 9.24–10.69%, the variation ranges of the toe angle, camber angle, and kingpin inclination angle under various mechanical parameters using the double-loop multi-objective particle swarm optimization algorithm were observed to be reduced by 50.45–79.39%, 1.84–4.24%, and 2.11–2.96%, respectively. Last but not least, the proposed double-loop multi-objective particle swarm optimization algorithm provided better handling, stability, and ride comfort values than the traditional multi-objective particle swarm optimization algorithm and the genetic algorithm.

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“…While this number focuses on adult cases, the fact remains that a majority of elective surgeries were affected by COVID-19, alluding to an unprecedented increase in cancellations and/or delays of both adult and pediatric surgeries. The medical dollars, hours and personnel wasted in delaying or canceling a case can be avoided by instituting the perioperative surgical home (PSH), especially during the new emergence of COVID-19, and with other chronic illnesses on the rise (i.e., obesity, diabetes) [2].…”

mentioning

confidence: 99%

The Perioperative Surgical Home in Pediatrics: Improve Patient Outcomes, Decrease Cancellations, Improve HealthCare Spending and Allocation of Resources during the COVID-19 Pandemic

et al. 2020

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Cancellations or delays in surgical care for pediatric patients that present to the operating room create a great obstacle for both the physician and the patient. Perioperative outpatient management begins prior to the patient entering the hospital for the day of surgery, and many organizations practice using the perioperative surgical home (PSH), incorporating enhanced recovery concepts. This paper describes changes in standard operating procedures caused by the COVID-19 pandemic, and proposes the expansion of PSH, as a means of improving perioperative quality of care in pediatric populations.

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Stochastic programming analysis and solutions to schedule overcrowded operating rooms in China

Cited by 31 publications

References 27 publications

Uncertainty in advance scheduling problem in operating room planning

Uncertainty in advance scheduling problem in operating room planning

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm

The Perioperative Surgical Home in Pediatrics: Improve Patient Outcomes, Decrease Cancellations, Improve HealthCare Spending and Allocation of Resources during the COVID-19 Pandemic

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