A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling

Astaraky, Davood; Patrick, Jonathan

doi:10.1016/j.ejor.2015.02.032

Cited by 61 publications

(34 citation statements)

References 36 publications

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“…With the growing increase in the demand for health care services and products as a result of the aging population, along with an increase in morbidity caused by environment deterioration and unhealthy lifestyles and advancements in technology, hospitals are forced to attend to the optimal management of health care resources . These mainly cover outpatient services, medical technologies, operation rooms (ORs), and beds, among which the typical revenue and cost of ORs account for over 60% and 30% of whole hospital income and outcomes, respectively .…”

Section: Introductionmentioning

confidence: 99%

A random forest and simulation approach for scheduling operation rooms: Elective surgery cancelation in a Chinese hospital urology department

Luo

Feng

Zhao

et al. 2018

Health Planning & Management

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Many hospitals encounter surgery cancelations for various reasons. We present a methodology applying data mining and simulation to optimize operating room (OR) scheduling in a urology department in West China Hospital. To the best of our knowledge, this is 1 of the first efforts to seek an optimal schedule solution based on cancelation risk of elective surgeries as well as OR allocation between elective and nonelective surgeries. First, chi-square test and random forest prediction modeling were used to predict potential elective surgeries with high cancelation risk, and the factors, including surgeon, number of days since admission of patient, first surgery or not, etc., that influence elective surgery cancelation were identified. Second, a simulation technology was designed to compare 7 different scheduling strategies. The results demonstrated that for elective surgery, cancelation rate low surgery first outperformed the others and increased the productivity of the ORs from 72% to 83%, while for nonelective surgery performed in a separate OR, there was no improvement because the supply was greater than necessary at present. However, in total, the selected strategies led to 7% higher productivity.

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Section: Introductionmentioning

confidence: 99%

A random forest and simulation approach for scheduling operation rooms: Elective surgery cancelation in a Chinese hospital urology department

Luo

Feng

Zhao

et al. 2018

Health Planning & Management

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“…Type of problem Simulation-based approach Linear programming approach Fleet management Powell (1987); Frantzeskakis and Powell (1990); Godfrey and Powell (2002); Simão et al ( , 2010; Maxwell et al (2010Maxwell et al ( , 2013; Schmid (2012) Inventory management Van Roy et al (1997), Lam et al (2007), , Enders et al (2010) Adelman and Klabjan (2012) Revenue management Gosavi et al (2002), Hing et al (2007) Zhang and Adelman (2009) Routing Marbach et al (2000) Adelman (2004) Healthcare scheduling Schütz and Kolisch (2012), Astaraky and Patrick (2015) Patrick et al (2008), Sauré et al (2012), Gocgun and Puterman (2014) whereas Q-learning methods concern the approximation of the optimal value function. Policy evaluation algorithms can be further classified as direct or indirect (Bertsekas 2011).…”

Section: List Of Approximate Dynamic Programming (Adp) Applications Cmentioning

confidence: 98%

Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions

Sauré

Patrick

Puterman

2015

INFORMS Journal on Computing

Self Cite

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W e present an approximate dynamic programming method based on simulation, policy iteration, a postdecision state formulation, and a logistic value function approximation. This method was developed as part of our efforts to determine whether nonlinear value function approximations could provide cost-effective policies for advance patient scheduling problems, and as a way of identifying the main advantages and disadvantages of using simulation versus linear programming to approximately solve dynamic capacity allocation problems. We first apply the proposed method to a queueing problem and then study a more practical application based on an advance multipriority patient scheduling problem. We investigate the quality and practical implications of the resulting appointment scheduling policies using simulation, and compare their performance to that of four other policies. Patient scheduling policies obtained by the new method not only depend on the number of appointments already booked on each day but also on the overall system workload. In particular, these policies provide lower discounted cost values and shorter average wait times for higher priority patients than policies directly obtained using linear programming and an affine value function approximation in the predecision state variables.

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“…Sometimes, this schedule is subjected to change, if any emergent patient needs to be operated on [10]. Discrete event simulation is a common approach used to study this step and to generate adequate schedules [22,23,26].…”

Section: Three-step Approachmentioning

confidence: 99%

“…Astaraky and Patrick [26] present a simulation based dynamic programming approach to determine a surgical schedule that makes the best use of operating rooms and recovery units. The authors develop a Least Squares Approximate Policy Iteration algorithm and test their model using data from a local hospital.…”

Section: Simulation Based Approachmentioning

confidence: 99%

Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming

Rafaliya²,

Baki

et al. 2015

Health Care Manag Sci

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Scheduling of surgeries in the operating rooms under limited competing resources such as surgical and nursing staff, anesthesiologist, medical equipment, and recovery beds in surgical wards is a complicated process. A well-designed schedule should be concerned with the welfare of the entire system by allocating the available resources in an efficient and effective manner. In this paper, we develop an integer linear programming model in a manner useful for multiple goals for optimally scheduling elective surgeries based on the availability of surgeons and operating rooms over a time horizon. In particular, the model is concerned with the minimization of the following important goals: (1) the anticipated number of patients waiting for service; (2) the underutilization of operating room time; (3) the maximum expected number of patients in the recovery unit; and (4) the expected range (the difference between maximum and minimum expected number) of patients in the recovery unit. We develop two goal programming (GP) models: lexicographic GP model and weighted GP model. The lexicographic GP model schedules operating rooms when various preemptive priority levels are given to these four goals. A numerical study is conducted to illustrate the optimal master-surgery schedule obtained from the models. The numerical results demonstrate that when the available number of surgeons and operating rooms is known without error over the planning horizon, the proposed models can produce good schedules and priority levels and preference weights of four goals affect the resulting schedules. The results quantify the tradeoffs that must take place as the preemptive-weights of the four goals are changed.

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A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling

Cited by 61 publications

References 36 publications

A random forest and simulation approach for scheduling operation rooms: Elective surgery cancelation in a Chinese hospital urology department

A random forest and simulation approach for scheduling operation rooms: Elective surgery cancelation in a Chinese hospital urology department

Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions

Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming

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