Uncertainty in advance scheduling problem in operating room planning

Kamran, Mehdi A.; Karimi, Behrooz; Dellaert, Nico

doi:10.1016/j.cie.2018.09.030

Cited by 37 publications

(17 citation statements)

References 47 publications

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“…The mathematical formulation of operating room planing and scheduling are presented in the huge majority of papers presented by [ 1 , 81 , 82 ]. This review will consider the mathematical formulation as it is modelled by [ 82 ] as advanced scheduling formulation. Lets ( R ) is the operating rooms, and .…”

Section: Operating Room Schedulingmentioning

confidence: 99%

Healthcare scheduling in optimization context: a review

et al. 2021

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This paper offers a summary of the latest studies on healthcare scheduling problems including patients’ admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients’ admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

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Section: Operating Room Schedulingmentioning

confidence: 99%

Healthcare scheduling in optimization context: a review

et al. 2021

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“…Constraint (11) calculates the amount of waiting time in every OR per scenario. The chance constraints (12) state that the surgeries assigned to an OR must be finished during the regular hours (i.e., no overtime) with high probability. Constraint (13) enforces the non-negativity of the second-stage decision variables.…”

Section: B Chance-constrained or Scheduling Problemmentioning

confidence: 99%

“…The remainder of this section presents another class of feasibility cuts derived from the solutions to the first-stage problem (M 1 ) or master problem (MP) that result in the violation of chance constraints (12).…”

Section: A Feasibility Cutsmentioning

confidence: 99%

“…Noorizadegan and Seifi [23] proposed a CCP model for the surgery planning problem with uncertain surgery durations. Kamran et al [12] proposed a two-stage stochastic model with chance constraints on OR overtime for the advance scheduling problem. Deng et al [5] developed a distributionally robust chance-constrained model for the OR scheduling problem.…”

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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“…Xiang [32] proposed a meta-heuristic approach by integrating Pareto sets and ant colony optimization (ACO) to solve a multi-objective combinatorial optimization problem with several objectives, e.g., minimizing patients' waiting time, reducing medical staff overtime, and increasing OR resource utilization. Kamran et al [21] proposed a multiple-objective problem, including minimizing the patients waiting time, tardiness, cancellation, block overtime, and the number of surgery days of each surgeon within the planning horizon. Sample average approximation (SAA) method and Benders decomposition technique were used to solve the proposed problem.…”

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confidence: 99%

Dynamic-programming-based heuristic for multi-objective operating theater planning

Liu¹,

Li²

2022

JIMO

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This study focuses on the assignment of surgical operations to operating room (OR) blocks to minimize not only the utilization cost of the OR blocks but also the number of patterns generated. We propose a dynamicprogramming-based heuristic to solve this problem. Using an iterative formulation, we separate the patterns individually. The computational results show that the proposed heuristic is efficient. Compared with "quasi-optimal" plans, the average cost gap ranges from 0.022 to 0.066 for various scenarios. A lower bound based on column generation procedure is developed. Results show that the average absolute difference of three scenarios are respectively 0.045, 0.081 and 0.115. According to evaluations based on performance indicators from the extant literature, the utilization ratio of the operating theater (OT) varies from 1% underutilization to 2% overutilization in the solutions yielded by the proposed heuristic. This study deals with a multi-objective optimization problem, the second objective of which plays a significant role in reducing the preparation cost, error probability, and staff fatigue in medical systems, where security and human issues are far more emphasized than in other sectors. To the best of our knowledge, our study is the first to investigate such a real-world multi-objective OT planning problem.

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Uncertainty in advance scheduling problem in operating room planning

Cited by 37 publications

References 47 publications

Healthcare scheduling in optimization context: a review

Healthcare scheduling in optimization context: a review

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

Dynamic-programming-based heuristic for multi-objective operating theater planning

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