A variability reduction method for the operating room scheduling problem under uncertainty using CVaR

Najjarbashi, Amirhossein; Lim, Gino J.

doi:10.1016/j.orhc.2019.01.001

Cited by 20 publications

(9 citation statements)

References 29 publications

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“…Assigning a smaller value for M can help tighten the feasible region for the LP relaxation of M DEF . Therefore, instead of setting a single large value for M , constraint-specific formulae used to calculate the big M values are developed for constraints (22) and (23) as shown below:…”

Section: B Chance-constrained or Scheduling Problemmentioning

confidence: 99%

“…We compare the performance of the proposed chanceconstrained model with the two models proposed in [22]: SDORS-EV and SDORS-CVaR. SDORS-EV is a stochastic programming model that attempts to optimize the expected value of OR overtime and patient waiting costs.…”

Section: A Comparing Ccp With Other Stochastic Modelsmentioning

confidence: 99%

“…SDORS-EV is a stochastic programming model that attempts to optimize the expected value of OR overtime and patient waiting costs. SDORS-CVaR is a risk-based model that minimizes the expected tail of overtime and waiting costs by using the CVaR function [22]. For simplicity, the following terminology is used in our experiments: CCP (chance-constrained), CVaR (SDORS-CVaR) and EV (SDORS-EV).…”

Section: A Comparing Ccp With Other Stochastic Modelsmentioning

confidence: 99%

“…However, the obtained solutions may show poor performance for problems displaying frequent changes in a less predictable manner [24]. A number of articles considered using the Conditional Value-at-Risk (CVaR) [27] to account for undesirable realizations of the uncertain parameters [15], [22], [29]. The CVaR function minimizes the expected tail of costs.…”

Section: Introductionmentioning

confidence: 99%

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A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem

Najjarbashi

Lim

2020

IEEE Access

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The required time for surgical interventions in operating rooms (OR) may vary significantly from the predicted values depending on the type of operations being performed, the surgical team, and the patient. These deviations diminish the efficient utilization of OR resources and result in the disruption of projected surgery start times. This paper proposes a two-stage chance-constrained model to solve the OR scheduling problem under uncertainty. The goal is to minimize the costs associated with OR opening and overtime as well as reduce patient waiting times. The risk of OR overtime is controlled using chance constraints. Numerical experiments show that the proposed model provides a better trade-off between minimizing costs and reducing solution variability when compared to two existing models in the literature. It is also shown that the three models converge as the overtime probability threshold approaches one. Moreover, it is observed that the individual chance constraints result in the opening of fewer rooms, lower waiting times, and shorter solution times when compared to that of joint chance constraints. A decomposition algorithm is applied that solves large test instances of the OR scheduling problem, that of which is known to be NP-hard. Strong valid inequalities are derived in order to accelerate the convergence speed. The proposed approach outperformed both a commercial solver and a basic decomposition algorithm after solving all test instances with up to 89 surgeries and 20 ORs in less than 48 minutes. INDEX TERMS Chance constraints, mixed-integer programming, operating room scheduling, two-stage stochastic programming, uncertainty.

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Section: B Chance-constrained or Scheduling Problemmentioning

confidence: 99%

Section: A Comparing Ccp With Other Stochastic Modelsmentioning

confidence: 99%

Section: A Comparing Ccp With Other Stochastic Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem

Najjarbashi

Lim

2020

IEEE Access

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“…Computational tests show that the minimization of the CVaR reduces the variability in performance at the expense of a slight increase in the expected performance. Najjarbashi and Lim () study the problem of determining an optimal assignment of patients to a set of operating rooms and, simultaneously, the sequence of surgeries to be performed. Considering that surgery durations are, in fact, stochastic, the authors present a scenario‐based MILP model that minimizes the CVaR of overtime and idle time costs.…”

Section: Schedulingmentioning

confidence: 99%

Conditional value‐at‐risk beyond finance: a survey

Filippi

Guastaroba

Speranza

2019

Int Trans Operational Res

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A large number of problems involve making decisions in an uncertain environment and, hence, with unknown outcomes. Optimization models aimed at controlling the trade‐off between risk and return in finance have been widely studied since the seminal work by Markowitz in 1952. In financial applications, shortfall or quantile risk measures are receiving ever‐increasing attention. Conditional value‐at‐risk (CVaR) is arguably the most popular of such measures. In the last decades, optimization models aimed at controlling risk have been applied to several application domains different from financial optimization. This survey provides an overview of the main contributions where CVaR is incorporated into an optimization approach and applied to a context different from financial engineering. The literature is classified following an application‐oriented perspective. The applications cover classical areas studied in operational research—such as supply chain management, scheduling, and networks—and less classical areas such as energy and medicine. For each area, concise paper excerpts are provided that convey the main ideas of the problems studied, and analyze how the CVaR has been used to cope with different sources of uncertainty. Finally, some open research directions are outlined.

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COVID19 epidemic outbreak: operating rooms scheduling, specialty teams timetabling and emergency patients' assignment using the robust optimization approach

et al. 2022

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The health care system is characterized by limited resources, including the physical facilities as well as skilled human resources. Due to the extensive fixed cost of medical facilities and the high specialization required by the medical staff, the problem of resource scarcity in a health care supply chain is much more acute than in other industries. In the pandemic of the Coronavirus, where medical services are the most important services in communities, and protective and preventive guidelines impose new restrictions on the system, the issue of resource allocation will be more complicated and significantly affect the efficiency of health care systems. In this paper, the problem of activating the operating rooms in hospitals, assigning active operating rooms to the COVID-19 and non-COVID-19 patients, assigning specialty teams to the operating rooms and assigning the elective and emergency patients to the specialty teams, and scheduling their operations is studied by considering the new constraints of protective and preventive guidelines of the Coronavirus. To address these issues, a mixed-integer mathematical programming model is proposed. Moreover, to consider the uncertainty in the surgery duration of elective and emergency patients, the stochastic robust optimization approach is utilized. The proposed model is applied for the planning of operating rooms in the cardiovascular department of a hospital in Iran, and the results highlight the role of proper management in supplying sufficient medical resources effectively to respond to patients and scheduled surgical team to overcome the pressure on hospital resources and medical staff results from pandemic conditions.

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A variability reduction method for the operating room scheduling problem under uncertainty using CVaR

Cited by 20 publications

References 29 publications

A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem

A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem

Conditional value‐at‐risk beyond finance: a survey

COVID19 epidemic outbreak: operating rooms scheduling, specialty teams timetabling and emergency patients' assignment using the robust optimization approach

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