A sequential stochastic mixed integer programming model for tactical master surgery scheduling

Kumar, Ashwani; Costa, Alysson M.; Fackrell, Mark; Taylor, Peter G.

doi:10.1016/j.ejor.2018.04.007

Cited by 32 publications

(24 citation statements)

References 13 publications

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“…At the microscale, they involve how the beds are configured, how the patients are placed, and how patients are admitted to the hospital. Relevant scholars have investigated and evaluated bed utilization efficiency in a certain area in Canada [ 16 , 17 ]. The study found that, of the 2007 patient study subjects, 14.2% of patients did not meet the admission criteria, and the total length of hospital stay was 14,194 days, of which 22.8% were unreasonable.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Chen

et al. 2020

Journal of Healthcare Engineering

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Bed resources are the platform in which most medical and health resources in the hospital play a role and carry the core functions of the health service system. How to improve the efficiency of the use of bed resources through scientific management measures and methods and ultimately achieve the optimization of overall health resources is the focus of hospital management teams. This paper analyzes the previous research models of knowledge related to queuing theory in medical services. From the perspective of the hospital and the patient, several indicators such as the average total number of people, the utilization rate of bed resources, the patient stop rate, and the patient average waiting time are defined to measure the performance of the triage queue calling model, which makes the patient queue more reasonable. According to the actual task requirements of a hospital, a Markov queuing strategy based on Markov service is proposed. A mathematical queuing model is constructed, and the process of solving steady-state probability based on Markov theory is analyzed. Through the comparative analysis of simulation experiments, the advantages and disadvantages of the service Markov queuing model and the applicable scope are obtained. Based on the theory of the queuing method, a queuing network model of bed resource allocation is established in principle. Experimental results show that the queuing strategy of bed resource allocation based on Markov optimization effectively improves resource utilization and patient satisfaction and can well meet the individual needs of different patients. It does not only provide specific optimization measures for the object of empirical research but also provides a reference for the development of hospital bed resource allocation in theory.

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Section: Introductionmentioning

confidence: 99%

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Chen

et al. 2020

Journal of Healthcare Engineering

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“…There are a variety of methods developed for tackling the uncertainties in surgical scheduling, among which the most common two are stochastic optimization and robust optimization . For the former, Kumar et al ( 2018 ) develop a stochastic mixed-integer programming model to balance the patient flow under downstream capacity constraints. Stochastic optimization considers hospitalization time to be stochastic, but assumes that the distribution is known, which might not be true in practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations

et al. 2022

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Operating Room (OR) management has been among the mainstream of hospital management research, as ORs are commonly considered as one of the most critical and expensive resources. The complicated connection and interplay between ORs and their upstream and downstream units has recently attracted research attention to focus more on allocating medical resources efficiently for the sake of a balanced coordination. As a critical step, surgical scheduling in the presence of uncertain surgery durations is pivotal but rather challenging since a patient cannot be hospitalized if a recovery bed will not be available to accommodate the admission. To tackle the challenge, we propose an overflow strategy that allows patients to be assigned to an undesignated department if the designated one is full. It has been proved that overflow strategy can successfully alleviate the imbalance of capacity utilization. However, some studies indicate that implementation of the overflow strategy exacerbates the readmission rate as well as the length of stay (LOS). To rigorously examine the overflow strategy and explore its optimal solution, we propose a Fuzzy model for surgical scheduling by explicitly considering downstream shortage, as well as the uncertainty of surgery duration and patient LOS. To solve the Fuzzy model, a hybrid algorithm (so-called GA-P) is developed, stemming from Genetic Algorithm (GA). Extensive numerical results demonstrate the plausible efficiency of the GA-P algorithm, especially for large-scale scheduling problems (e.g., comprehensive hospitals). Additionally, it is shown that the overflow cost plays a critical role in determining the efficiency of the overflow strategy; viz., benefits from the overflow strategy can be reduced as the overflow cost increases, and eventually almost vanishes when the cost becomes sufficiently large. Finally, the Fuzzy model is tested to be effective in terms of simplicity and reliability, yet without cannibalizing the patient admission rate.

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“…Surgery types are often sets of surgeries that are similar in economic and resource usage perspective. This is the case in, e.g., Bovim et al (2020); Anjomshoa et al (2018); Kumar et al (2018); Dellaert and Jeunet (2017); Visintin et al (2016); Cappanera et al (2014); Banditori et al (2013);van Oostrum et al (2008). Once again, decisions on how much operating room time to assign to each specialty are not usually considered or do not take into account issues such as equity or waiting list length.…”

Section: Literature Reviewmentioning

confidence: 99%

Flexible master surgery scheduling: combining optimization and simulation in a rolling horizon approach

et al. 2021

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Operating room managers are facing increasingly complex challenges, namely in complying with waiting time targets before surgery. This paper proposes a framework that combines optimization and simulation to generate dynamic master surgery schedules for a long planning horizon, in which the schedules are optimized by an integer programming model and the demand levels are modelled using the simulation model. The developed approach allows the resulting operating room plan to balance waiting lists as it assigns more time to the specialties with higher demand in terms of time needed to perform all the surgeries in the corresponding waiting lists. The analysis of the results obtained for the proposed flexible rolling horizon approach were proven robust, and were compared to static and flexible long-term approaches, the former not allowing flexibility and the latter using a deterministic update of the demand. Considering throughput, tardiness and waiting time, the flexible rolling horizon approach showed the best results, while the static one had the worst results.

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A sequential stochastic mixed integer programming model for tactical master surgery scheduling

Cited by 32 publications

References 13 publications

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations

Flexible master surgery scheduling: combining optimization and simulation in a rolling horizon approach

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