2,291 research outputs found
Stochastic surgery selection and sequencing under dynamic emergency break-ins
Anticipating the impact of urgent emergency arrivals on operating room schedules remains methodologically and computationally challenging. This paper investigates a model for surgery scheduling, in which both surgery durations and emergency patient arrivals are stochastic. When an emergency patient arrives he enters the first available room. Given the sets of surgeries available to each operating room for that day, as well as the distributions of the main stochastic variables, we aim to find the per-room surgery sequences that minimise a joint objective, which includes over- and under-utilisation, the amount of cancelled patients, as well as the risk that emergencies suffer an excessively long waiting time. We show that a detailed analysis of emergency break-ins and their disruption of the schedule leads to a lower total cost compared to less sophisticated models. We also map the trade-off between the threshold for excessive waiting time, and the set of other objectives. Finally, an efficient heuristic is proposed to accurately estimate the value of a solution with significantly less computational effort.Anticipating the impact of urgent emergency arrivals on operating room schedules remains methodologically and computationally challenging. This paper investigates a model for surgery scheduling, in which both surgery durations and emergency patient arrivals are stochastic. When an emergency patient arrives he enters the first available room. Given the sets of surgeries available to each operating room for that day, as well as the distributions of the main stochastic variables, we aim to find the per-room surgery sequences that minimise a joint objective, which includes over- and under-utilisation, the amount of cancelled patients, as well as the risk that emergencies suffer an excessively long waiting time. We show that a detailed analysis of emergency break-ins and their disruption of the schedule leads to a lower total cost compared to less sophisticated models. We also map the trade-off between the threshold for excessive waiting time, and the set of other objectives. Finally, an efficient heuristic is proposed to accurately estimate the value of a solution with significantly less computational effort.A
Operating room planning and scheduling: A literature review.
This paper provides a review of recent research on operating room planning and scheduling. We evaluate the literature on multiple fields that are related to either the problem setting (e.g. performance measures or patient classes) or the technical features (e.g. solution technique or uncertainty incorporation). Since papers are pooled and evaluated in various ways, a diversified and detailed overview is obtained that facilitates the identification of manuscripts related to the reader's specific interests. Throughout the literature review, we summarize the significant trends in research on operating room planning and scheduling and we identify areas that need to be addressed in the future.Health care; Operating room; Scheduling; Planning; Literature review;
Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS
We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making
Multi-objective Operating Room Planning and Scheduling
abstract: Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task due to the conflicting objectives and the uncertain nature of surgeries. In this dissertation, three different methodologies are developed to address OR planning and scheduling problem. First, a simulation-based framework is constructed to analyze the factors that affect the utilization of a catheterization lab and provide decision support for improving the efficiency of operations in a hospital with different priorities of patients. Both operational costs and patient satisfaction metrics are considered. Detailed parametric analysis is performed to provide generic recommendations. Overall it is found the 75th percentile of process duration is always on the efficient frontier and is a good compromise of both objectives. Next, the general OR planning and scheduling problem is formulated with a mixed integer program. The objectives include reducing staff overtime, OR idle time and patient waiting time, as well as satisfying surgeon preferences and regulating patient flow from OR to the Post Anesthesia Care Unit (PACU). Exact solutions are obtained using real data. Heuristics and a random keys genetic algorithm (RKGA) are used in the scheduling phase and compared with the optimal solutions. Interacting effects between planning and scheduling are also investigated. Lastly, a multi-objective simulation optimization approach is developed, which relaxes the deterministic assumption in the second study by integrating an optimization module of a RKGA implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for Pareto optimal solutions, and a simulation module to evaluate the performance of a given schedule. It is experimentally shown to be an effective technique for finding Pareto optimal solutions.Dissertation/ThesisPh.D. Industrial Engineering 201
TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING
The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses
An approximate dynamic programming approach to the admission control of elective patients
In this paper, we propose an approximate dynamic programming (ADP) algorithm
to solve a Markov decision process (MDP) formulation for the admission control
of elective patients. To manage the elective patients from multiple specialties
equitably and efficiently, we establish a waiting list and assign each patient
a time-dependent dynamic priority score. Then, taking the random arrivals of
patients into account, sequential decisions are made on a weekly basis. At the
end of each week, we select the patients to be treated in the following week
from the waiting list. By minimizing the cost function of the MDP over an
infinite horizon, we seek to achieve the best trade-off between the patients'
waiting times and the over-utilization of surgical resources. Considering the
curses of dimensionality resulting from the large scale of realistically sized
problems, we first analyze the structural properties of the MDP and propose an
algorithm that facilitates the search for best actions. We then develop a novel
reinforcement-learning-based ADP algorithm as the solution technique.
Experimental results reveal that the proposed algorithms consume much less
computation time in comparison with that required by conventional dynamic
programming methods. Additionally, the algorithms are shown to be capable of
computing high-quality near-optimal policies for realistically sized problems
Implications of Non-Operating Room Anesthesia Policy for Operating Room Efficiency
This thesis focuses on examining the use of Non-Operating Room Anesthesia (NORA) policy in
Operating Room (OR) scheduling. A NORA policy involves a practice whereby the administration
of anesthesia stage is performed outside the OR. The goal of the thesis is to determine whether
NORA policy can improve OR efficiency measured by the performance of total costs, which
consists of a weighted sum of patient waiting time, OR overtime and idle time. A simulation
optimization method is adopted to find near-optimal schedules for elective surgeries in an
outpatient setting. The results of a traditional OR scheduling model, where all stages of the surgery
are performed in the OR, will be compared to the results of a NORA OR model where the initial
anesthesia stage is performed outside of the OR. Two cases are considered for the NORA model
given the decrease on mean durations: (1) a model with the same number of surgery appointments
and shorter session length and (2) a models with the same session length and more surgery
appointments. . The impact of a NORA policy on OR performance is further analyzed by
considering scenarios that capture Surgery duration variability and mean surgery durations which
are two traits for surgeries that have been shown to impact OR performance. This thesis aims to
investigate how a NORA policy performs when standard deviations and mean surgery durations
change. The results show that NORA policy can improve OR efficiency in all settings
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