A MIP Model for Rolling Horizon Surgery Scheduling

Luo, Li; Luo, Yong; You, Yang; Cheng, Yuanjun; Shi, Yujun; Gong, Renrong

doi:10.1007/s10916-016-0490-9

Cited by 15 publications

(5 citation statements)

References 20 publications

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“…Patient arrivals are determined by many variables and are extremely unpredictable. However, research shows that machine learning algorithms can signifi cantly improve time prediction performance [19,25,28,38] and a number of proposed solutions is growing [7,14,24,26,41].…”

Section: Arrival Time To the Hedmentioning

confidence: 99%

Development of a Machine-Learning Algorithm for Predicting the Arrival Time and Assessing Factors Influencing Patient Arrival at Hospital Emergency Department – Preliminary Study

Karpiel,

Zięba

2023

PJAMBP

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Healthcare is one of the most important topics affecting society. Emergency Departments (ED) play an important role in the patient treatment cycle, providing immediate and primary health care as well as access to services offered by hospitals. From a hospital's perspective, it is crucial that emergency departments are well organized in each hospital sector. One of the most important yet overlooked problems in the medical industry is emergency department congestion. An attempt was made to estimate and predict, using predictive models and machine learning algorithm techniques, the impact of factors on the arrival time of patients at hospital emergency department and to verify the effectiveness of teaching the models, along with a comparison which one is the best in predicting the phenomena. Research materials were source data from the 2018 National Hospital Ambulatory Medical Care Survey (NHAMCS) – Emergency Department. In this paper, a study was conducted to search for factors that could affect the frequent arrival of patients at emergency departments. A tool for predicting the expected arrival time, taking into account the current arduous conditions/factors in the ED, has the potential to improve situational awareness and contribute to mitigating problems related to congestion. It appears that the model can be used to enhance decision support systems by determining patient arrival times. The results obtained in the random forest regression algorithm are estimated at R2 of 86% and RMSE of 5.45. For research purposes, when attempting to analyze on a sample and reliable hospital department database, the random forest regressor method identified the most relevant factors such as initial vital signs. The results obtained allow for a broader view in the context of assessing the prevailing situation in the emergency department. In this case – examining the most relevant factors influencing patient arrival times. With the results of high effectiveness, an algorithm can be designed to assist emergency departments in proper monitoring of existing problems.

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Section: Arrival Time To the Hedmentioning

confidence: 99%

Development of a Machine-Learning Algorithm for Predicting the Arrival Time and Assessing Factors Influencing Patient Arrival at Hospital Emergency Department – Preliminary Study

Karpiel,

Zięba

2023

PJAMBP

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“…Xiang and Li [ 8 ] proposed and defined a short-term surgery scheduling problem to optimize OR management and reduce operation costs based on a survey of the OR operation in a typical hospital in China. Luo et al [ 9 ] proposed a rolling horizon mixed-integer programming model for scheduling. Considering the day-to-day demand for surgery, they developed a nonrolling scheduling model (NRSM) and a rolling horizon scheduling model (RSM) to balance the occupancy time among operating rooms.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Day Surgery Scheduling and Optimization in Large Public Hospitals in China: A Three-Station Job Shop Scheduling Problem

Xue

Zhang

Luo

et al. 2022

Journal of Healthcare Engineering

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Day surgery scheduling allocates hospital resources to day surgical cases and decides on the time to perform the surgeries in the day surgery center (DSC). Based on the day surgery service process of large public hospitals in China, we found that the service efficiency of the process depends on the utilization of hospital resources efficiently and could be optimized through day surgery scheduling. We described it as a flexible flow shop owing to the three-station nature of surgery. Allocating all types of hospital resources to the three stations and determining the length of time for each stage during surgery are crucial to improving the efficiency of DSC. This paper integrates a three-station job shop scheduling problem (JSSP) into the day surgery scheduling and optimization problem. The JSSP was formulated as a mixed-integer linear programming model, and the elicitation of the model for scheduling surgeries with different priorities in the DSC is discussed. The model illustrated a case study of the DSC within West China Hospital (WCH). Numerical experiments based on the genetic algorithm design were conducted. Compared to the other optimization strategies, we proposed that the three-station job shop scheduling strategy (TSJS) could not only improve the efficiency and reduce the waiting time of the patients of the DSC in large public hospitals in China but also allow for timely scheduling adjustments during the advent of emergency surgeries.

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“…To adapt to the changing waiting list we apply a rolling horizon scheduling approach. In the literature, daily rescheduling with a rolling horizon model produces less OR idle time and increases utilisation compared to a non-rolling horizon model (Luo et al, 2016). In their recent review of OT planning and scheduling, Zhu et al (2018) indicate that there is a definite need for longer-term (several weeks) scheduling approaches (as opposed to daily approaches) that incorporate volatility.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated rolling horizon approach to increase operating theatre efficiency

Spratt

Kozan

2020

J Sched

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Demand for healthcare is increasing rapidly. To meet demand, we must improve the efficiency of our public health services. We present a mixed integer programming (MIP) formulation that simultaneously tackles the integrated Master Surgical Schedule (MSS) and Surgical Case Assignment (SCA) problems. We consider volatile surgical durations and non-elective arrivals whilst applying a rolling horizon approach to adjust the schedule after cancellations, equipment failure, or new arrivals on the waiting list. A case study of an Australian public hospital with a large surgical department is the basis for the model. The formulation includes significant detail and provides practitioners with a globally implementable model. We produce good feasible solutions in short amounts of computational time with a constructive heuristic and two hyper metaheuristics. Using a rolling horizon schedule increases patient throughput and can help reduce waiting lists.

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A MIP Model for Rolling Horizon Surgery Scheduling

Cited by 15 publications

References 20 publications

Development of a Machine-Learning Algorithm for Predicting the Arrival Time and Assessing Factors Influencing Patient Arrival at Hospital Emergency Department – Preliminary Study

Development of a Machine-Learning Algorithm for Predicting the Arrival Time and Assessing Factors Influencing Patient Arrival at Hospital Emergency Department – Preliminary Study

Day Surgery Scheduling and Optimization in Large Public Hospitals in China: A Three-Station Job Shop Scheduling Problem

An integrated rolling horizon approach to increase operating theatre efficiency

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