A two-phase optimization model combining Markov decision process and stochastic programming for advance surgery scheduling

Zhang, Jian; Dridi, Mahjoub; Moudni, Abdellah El

doi:10.1016/j.cie.2021.107548

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…One of the major problems associated with operational surgery scheduling is uncertainty in terms of surgery durations and patient arrivals. Various operations research methods have been applied to deal with the uncertainty in surgery scheduling, e.g., simulation (Ozen et al 2016, Samudra et al 2017, Bai et al 2022, Wang et al 2022, stochastic optimization models (Rath et al 2017, Khaniyev et al 2020, Zhang et al 2021, Bai et al 2022, robust optimization (Rath et al 2017, Bandi andGupta 2020), heuristics (Freeman et al 2016, Jung et al 2019, Khaniyev et al 2020, Bai et al 2022 and Markov decision process (Huh et al 2013, Liu et al 2019, Zhang et al 2021. Our research also uses MDP, but we are different from the literature in the following aspects.…”

Section: Or Pooling or Partitioning Between Different Surgery Typesmentioning

confidence: 99%

Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department

Wang,

Demeulemeester,

Vansteenkiste

et al. 2024

European Journal of Operational Research

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Section: Or Pooling or Partitioning Between Different Surgery Typesmentioning

confidence: 99%

Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department

Wang,

Demeulemeester,

Vansteenkiste

et al. 2024

European Journal of Operational Research

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“…Equation (4) represents the patients who are still occupying the beds on the day t . Equation (5) calculates the total number of patients in the ICU on the day t . Equation (6) indicates that the number of elective patients and current ICU patients does not exceed Q .…”

Section: Allocation Model Of the First Stagementioning

confidence: 99%

“…The operative uses the corresponding upstream resources and the postoperative uses the corresponding downstream resources 4 . Given that most upstream resources are relatively expensive and scarce, especially for the use of operating rooms (ORs), the mainstream of extant research focuses on the planning issues of upstream resources, while simply assuming ample sufficiency of downstream resources (e.g., inpatient beds) 5,6 . However, a shortage of downstream resources, such as inpatient or ICU beds, not only hinders the timely treatment of patients but also adversely impacts the utilization of related resources in the operating rooms.…”

mentioning

confidence: 99%

Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty

Wan,

Fondrevelle,

Wang

et al. 2023

Sci Rep

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Due to the impact of COVID-19, a significant influx of emergency patients inundated the intensive care unit (ICU), and as a result, the treatment of elective patients was postponed or even cancelled. This paper studies ICU bed allocation for three categories of patients (emergency, elective, and current ICU patients). A two-stage model and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to obtain ICU bed allocation. In the first stage, bed allocation is examined under uncertainties regarding the number of emergency patients and their length of stay (LOS). In the second stage, in addition to including the emergency patients with uncertainties in the first stage, it also considers uncertainty in the LOS of elective and current ICU patients. The two-stage model aims to minimize the number of required ICU beds and maximize resource utilization while ensuring the admission of the maximum number of patients. To evaluate the effectiveness of the model and algorithm, the improved NSGA-II was compared with two other methods: multi-objective simulated annealing (MOSA) and multi-objective Tabu search (MOTS). Drawing on data from real cases at a hospital in Lyon, France, the NSGA-II, while catering to patient requirements, saves 9.8% and 5.1% of ICU beds compared to MOSA and MOTS. In five different scenarios, comparing these two algorithms, NSGA-II achieved average improvements of 0%, 49%, 11.4%, 9.5%, and 17.1% across the five objectives.

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“…Considering that conventional dynamic programming algorithms cannot efficiently solve MDP models for real-sized problems, Zhang 8 et al develop an approximate dynamic programming approach that combines recursive least-squares temporal difference learning and mixed integer programming. Considering that the pure mathematical programming models commonly used mostly focus on the short-term optimization of surgery schedules, Zhang 9 et al propose a novel two-phase optimization model that combines Markov decision process and stochastic programming to improve the longterm performance of surgery schedules. The objective is to minimize the patient-related costs incurred by performing and postponing surgeries as well as the hospital-related costs caused by utilization of surgical resources.…”

Section: Related Workmentioning

confidence: 99%

Peak traffic statistics and prediction for Markov process-based scheduling models

Wu,

Yang

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Many scheduling models have a finite number of states, mostly described using Markov processes. These models analyze the possible states of the system at different times. The scheduling algorithms for practical applications are combined with an engineering background and cannot be applied to other systems. The statistics and prediction of peaks also need to be combined with the needs of specific problems. In this paper, a peak state identification algorithm is designed based on Markov chains combined with machine learning methods. The algorithm can find the peak states from approximating the laws of the system itself in different cycles and can be used to predict the peak states in that cycle. We build a distance distribution to filter the suitable states and rank the different states by statistical analysis. We verify the accuracy of the algorithm by inputting random peak states, and the results show a high identification accuracy at different peak amounts. The algorithm can be applied to scheduling models with fixed nodes and with traffic demand and it has high accuracy in long runs.

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A two-phase optimization model combining Markov decision process and stochastic programming for advance surgery scheduling

Cited by 8 publications

References 45 publications

Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department

Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department

Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty

Peak traffic statistics and prediction for Markov process-based scheduling models

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