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This paper addresses a real-world Integrated Operating Room Planning and Scheduling (IORPS) problem encountered by a local hospital in Naples, characterized by stringent emergency management constraints, requiring treatment initiation within a 20-minute time-window. We tackle this problem by an original Integer Linear Programming formulation, capable of dealing with different operating room management strategies (open, block and block-modified). Our work differs from conventional cost-focused models by adopting a patient-oriented objective function, aligning with public hospitals obligations. The proposed method has been validated using real-world data provided by the hospital. The performed experimentation demonstrates the efficiency of the approach, capable of determining the optimal solution within an acceptable computation time that aligns with hospital requirements. Moreover, it also highlights the relevance of using our optimization approach to reduce delays in emergency responsiveness. This confirms its practical usage as a substitute for the current manual procedure. [ABSTRACT FROM AUTHOR]

The problem studied in this paper is elective surgery scheduling, with resource constraints in each of the three following stages: preoperative, perioperative, and postoperative stages. With the integrated availability of hospital beds in wards and operating rooms, the aim is to determine operation start times of surgeries and allocate the hospital beds to patients while getting patients treated as soon as possible. This task is crucial in providing timely treatments for the patients while ensuring the hospital’s resource utilization balance. For the problem, we first formulate it as mixed-integer programming, which is NP-complete. Then, we propose several heuristics to overcome the long computation time. To make the solution better, we also propose improved algorithms. Finally, we conduct a series of numerical studies to illustrate the efficiency of our proposed algorithms and examine the impact of the number of jobs, beds, and surgery blocks on the performance measure. Computational experiments showed the superior performance of our heuristics in makespan. [ABSTRACT FROM AUTHOR]

Motivated by a real problem in a big hospital in China, we study a daily surgery scheduling problem with operating room eligibility and dedicated surgeon. We model the problem as a parallel machine scheduling problem with machine and resource constraints to minimise the makespan, and innovatively propose an adaptive composite dispatching method to deal with such a strongly NP-hard problem. The dispatching rule is a combination of three popular rules LPT, LFJ and LRW, each of which can deal with some special features of the scheduling problems, and the scaling parameters are estimated through a statistical model learned from historical data. The adaptive composite dispatching method is easy-to-implement, fast, adaptable, robust and flexible. To examine the performance of the proposed solution approach, we first carry out a series of computational experiments showing that the adaptive composite dispatching method works very well compared to the optimal solution. Using a real data set, we further conduct a case study showing that our solution approach can improve the current practice by significantly shortening the makespan and reducing the overnight work. [ABSTRACT FROM AUTHOR]

One of the critical issues in healthcare management is the operating room (OR) scheduling problem. Solutions to this problem consider surgery durations and allocate elective surgeries to OR sessions in order to create surgical lists of high quality. Determining the quality of a surgical list is a key undertaking within OR scheduling and is the focus of this research. Currently, probability- and/or expectation-based measures of surgical lists are used instead of statistical distributions of surgery lists to measure quality. The use of multiple measures, e.g., a combination of expectation and probability to assess a surgical list, complicates OR scheduling, so we introduce a new single measure – the OR scheduling metric – for evaluating surgical lists before their realisations, i.e., for use within OR scheduling. We apply the OR scheduling metric to an actual elective dataset and use simulation to demonstrate its use, including customised scheduling rules. We recommend the adoption of a benchmarked OR scheduling metric by the elective surgical services in hospitals with expected practical benefits in the long run, i.e., simpler OR scheduling and more desirable room utilisation, to be similar to that observed in our simulations. [ABSTRACT FROM AUTHOR]

An algorithm for the two-stage robust optimization surgery-to-operating room allocation problem is presented. The second-stage problem is an integer linear program whose convex hull is approximated using three types of specialized valid inequalities and Chvátal-Gomory cuts. The resulting linear relaxation of the second-stage problem is then dualized and integrated into the first-stage problem. The resulting mixed integer linear program, which is an approximation of the original problem, is then solved using a commercial solver. If the solution of this model is not optimal for the second-stage problem, valid inequalities for the second-stage problem are generated, yielding a type of column-generation based approach that we refer to as the sequential follower refinement (SFR) algorithm. Data from an academic medical center are used to compare the computational performance of SFR with the constraint and column generation (C&CG) algorithm, which is the only exact approach that has been specifically applied for this problem in the literature. An extensive numerical study of SFR and its computational characteristics is presented that shows that SFR yields better-quality solutions compared with C&CG, even as the termination criterion of SFR is met much sooner, especially for problems involving higher number of surgeries. History: Accepted by Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0191) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0191). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the causes and prevalence of elective surgery cancellations in the operating room, and the clinical outcomes of affected patients. Methods: This prospective, cross-sectional study assessed the prevalence and causes of elective surgery cancellations once patients are in the operating room. A tertiary academic referral center hosted the study between January 2022 and January 2023. The study sample consisted of 7,482 adult patients scheduled for elective surgeries and taken to the operating room. The 7,415 completed procedures were in Group 2, whereas the 67 cancelled surgeries were in Group 1. Patients were divided into two groups on the basis of whether their surgeries were completed or cancelled. Factors such as age, American Society of Anesthesiologists (ASA) status, and surgical department were analyzed. The two groups were compared on the basis of age, ASA status, surgical department, and surgery time (month and day). Results: Elective surgery cancellations occurred in the operating room at a rate of 0.9%. Group 1 was substantially older than Group 2 (p<0.001). Group 1 had a larger number of ASA III patients (p<0.001). The department with the highest cancellation rate was ophthalmology (2.5%), followed by general surgery (2.1%), urology (1.5%), and ear, nose, and throat (1.4%). It was possible to avoid 59.7% of cancelations. Conclusion: The study revealed a 0.9% prevalence rate of elective surgery cancelations in the operating room. Older age and higher ASA status greatly influenced these cancellations. Optimized surgery scheduling and patient assessment processes may prevent many of these cancellation. [ABSTRACT FROM AUTHOR]

Objective: For patient safety, maintaining hemodynamic stability during surgical procedures is critical. Dynamic indices [such as systolic pressure variation (SPV) and pulse pressure variation (PPV)], have recently seen an increase in use. Given the risks associated with such invasive techniques, there is growing interest in non-invasive monitoring methods-and plethysmographic waveform analysis. However, many such non-invasive methods involve intricate calculations or brand-specific monitors. This study introduces the simple systolic ratio (SSR), derived from pulse oximetry tracings, as a non-invasive method to assess fluid responsiveness. Methods: This prospective observational study included 25 adult patients whose SPV, PPV, and SSR values were collected at 30-min intervals during open abdominal surgery. The SSR was defined as the ratio of the tallest waveform to the shortest waveform within pulse tracings. The correlations among SSR, SPV, and PPV were analyzed. Additionally, anaesthesia specialists visually assessed pulse oximetry tracings to determine fluid responsiveness using the SSR method. Results: Strong correlations were observed between SSR and both SPV (r = 0.715, P < 0.001) and PPV (r = 0.702, P < 0.001). Receiver operator curve analysis identified optimal SSR thresholds for predicting fluid responsiveness at 1.47 for SPV and 1.50 for PPV. A survey of anaesthesia specialists using the SSR method to visually assess fluid responsiveness produced an accuracy rate of 83%. Conclusion: Based on the strong correlations it exhibits with traditional markers, SSR has great potential as a clinical tool, especially in resource-limited settings. However, further research is needed to establish its role, especially as it pertains to its universal applicability across monitoring devices. [ABSTRACT FROM AUTHOR]

Purpose: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data. Methods: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC). Results: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22). Conclusion: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) account for a significant proportion of the costs of maintaining a hospital. Surgery scheduling is optimised to reduce the costs in ORs. However, an important issue that directly affects surgery scheduling is often neglected, i.e. surgeon collaboration. In reality, a surgery requires two surgeons: the main surgeon and an assistant. The surgeon of one surgery might be assigned as the assistant of another surgery. Thus, this paper proposes a two-stage robust model that considers such collaborations. In Stage 1, it determines surgery allocations and surgeon assignments, while in Stage 2, the start time of each surgery is decided. The properties of the solutions are detected, based on which we develop a simple algorithm to solve the two-stage model. In addition, the column-and-constraint generation algorithm is applied to solve the model with a large number of surgeries. The numerical experimentation shows the good performances of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the scheduling of elective surgeries in an operation theater that also treats emergent patients with priority when they arrive randomly. Two types of problems are considered: The first is in the short-term, the number of the patients for elective surgeries are fixed, all waiting at time zero for operations. The problem is to find the optimal policy to determine the order of all operations in waiting. The second is in the long-term, patients requiring surgeries of different types arrive randomly, and the problem is to determine the optimal policy to select the next elective surgery to operate. Optimal policies are derived for both types of problems based on the theory and techniques of stochastic scheduling and open bandit processes. [ABSTRACT FROM AUTHOR]

Patient features such as gender, age, and underlying disease are crucial to improving the model fidelity of surgery duration. In this paper, we study a robust surgery scheduling problem augmented by patient feature segmentation. We focus on the surgery‐to‐operating room allocations for elective patients and future emergencies. Using feature data, we classify patients into different types using machine learning methods and characterize the uncertain surgery duration via a feature‐based cluster‐wise ambiguity set. We propose a feature‐driven adaptive robust optimization model that minimizes an overtime riskiness index, which helps mitigate both the magnitude and probability of working overtime. The model can be reformulated as a second‐order conic programming problem. From the reformulation, we find that minimizing the overtime riskiness index is equivalent to minimizing a Fano factor. This makes our robust optimization model easily interpretable to healthcare practitioners. To efficiently solve the problem, we develop a branch‐and‐cut algorithm and introduce symmetry‐breaking constraints. Numerical experiments demonstrate that our model outperforms benchmark models in a variety of performance metrics. [ABSTRACT FROM AUTHOR]

The rapid population growth has resulted in ever-growing demand for healthcare services, which forces managers to use costly resources such as operating rooms effectively. The surgery-scheduling problem is a general title that is attributed to such problems as patient selection and sequencing of surgeries at the operational level, setting their start times, and assigning resources. Hospital managers usually encounter elective surgeries that can be delayed slightly and emergency surgeries whose arrivals are unexpected, and most of them need quick access to operating rooms. Reserving operating room capacity for handling incoming emergency surgeries is expensive. Moreover, emergency surgeries cannot afford long waiting times. This paper deals with the problem of surgery scheduling in the presence of emergency surgeries with a focus on balancing the efficient use of operating room capacity and responsiveness to emergency surgeries. We proposed a new algorithm for surgery scheduling with a specific operating room capacity planning and analyzed it using simulation method based on real data. This algorithm respects working hours and availability of staff and other resources in a surgical suite. [ABSTRACT FROM AUTHOR]

Determining the optimal surgical case start times is a challenging stochastic optimization problem that shares a key feature with many other healthcare operations problems. Namely, successful problem solutions require using a vast array of available historical data to create distributions that accurately capture a case duration's uncertainty for integration into an optimization model. Distribution fitting is the conventional approach to generate these distributions, but it can only employ a limited, aggregate portion of the detailed patient features available in Electronic Medical Records systems today. If all the available information can be taken advantage of, then distributions individualized to every case can be constructed whose precision would support higher quality solutions in the presence of uncertainty. Our individualized stochastic optimization framework shows how the quantile regression forest (QRF) method predicts individualized distributions that are integrable into sample-average approximation, robust optimization, and distributionally robust optimization models for problems like surgery scheduling. In this paper, we present some related theoretical performance guarantees for each formulation. Numerically, we also study our approach's benefits relative to three other traditional models using data from Memorial Sloan Kettering Cancer Center in New York, NY, USA. [ABSTRACT FROM AUTHOR]

Resource coordination in surgical scheduling remains challenging in health care delivery systems. This is especially the case in highly-specialized settings such as coordinating Intraoperative Neurophysiologic Monitoring (IONM) resources. Inefficient coordination yields higher costs, limited access to care, and creates constraints to surgical quality and outcomes. To maximize utilization of IONM resources, optimization-based algorithms are proposed to effectively schedule IONM surgical cases and technologists and evaluate staffing needs. Data with 10 days of case volumes, their surgery durations, and technologist staffing was used to demonstrate method effectiveness. An iterative optimization-based model that determines both optimal surgery and technologist start time (operational scenario 4) was built in an Excel spreadsheet along with Excel's Solver settings. It was compared with current practice (operational scenario 1) and optimization solution on only surgery start time (operational scenario 2) or technologist start time (operational scenario 3). Comparisons are made with respect to technologist overtime and under-utilization time. The results conclude that scenario 4 significantly reduces overtime by 74% and under-utilization time by 86% as well as technologist needs by 10%. For practices that do not have flexibility to alter surgeon preference on surgery start time or IONM technologist staffing levels, both scenarios 2 and 3 also result in substantial reductions in technologist overtime and under-utilization. Moreover, IONM technologist staffing options are discussed to accommodate technologist preferences and set constraints for surgical case scheduling. All optimization-based approaches presented in this paper are able to improve utilization of IONM resources and ultimately improve the coordination and efficiency of highly-specialized resources. [ABSTRACT FROM AUTHOR]

The stochastic high-patient-throughput surgery scheduling problem under a limited number of staffed ward beds is addressed in this paper. This work proposes a novel way to minimize the risk of last-minute cancellations by bounding the likelihood of exceeding the staffed ward beds. Given historical data, it is possible to determine an empirical distribution for the length of stay in the ward. Then, for any given combinations of patients, one can estimate the likelihood of exceeding the number of staffed ward beds using Monte Carlo sampling. As these ward patient combinations grow exponentially, an alternative, more efficient, worst-case robust ward optimization model is compared. An extensive data set was collected from the National University Hospital of Iceland for computational experiments, and the models were compared with actual scheduling data. The models proposed achieve high quality solutions in terms of overtime and risk of overflow in the ward. [ABSTRACT FROM AUTHOR]

In this article, we address a two-stage scheduling problem that requires coordination between clinical and surgical appointments for specialized surgeries. First, patients have a clinical appointment with a surgeon to determine whether they are an appropriate candidate for the surgical procedure. Subsequently, if the decision to pursue the surgery is made the patient undergoes the procedure on a later date. However, the scheduling process aims to book both the clinical and surgical appointments for a patient at the time of the initial appointment request. Two sources of uncertainty make this scheduling process challenging: (i) the patient may or may not need surgery after the clinical appointment and (ii) the surgery duration for each patient and procedure is unknown. We present a Distributionally Robust Optimization (DRO) approach for coordinating clinical and surgical appointments under these uncertainties. A case study of the Transcatheter Aortic Valve Replacement procedure at Mayo Clinic, Rochester, MN is presented. Numerical results include comparisons with the current practice and four heuristic scheduling policies from the literature. Results show that the DRO-based scheduling policies lead to lower total surgeon idle-time and overtime per day. The proposed policies also restrict the under and over utilization of clinical capacity. [ABSTRACT FROM AUTHOR]

Background: Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure.Objective: This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs.Methods: A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. Multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process.Results: Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well.Conclusions: Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Surgical theater (ST) operations planning is a key subject in the healthcare management literature, particularly the scheduling of procedures in operating rooms (ORs). The OR scheduling problem is usually approached using mathematical modeling and made available to ST managers through dedicated software. Regardless of the large body of knowledge on the subject, OR scheduling models rarely consider the integration of OR downstream and upstream facilities and resources or validate their propositions in real life, rather using simulated scenarios. We propose a heuristic to sequence surgeries that considers both upstream and downstream resources required to perform them, such as surgical kits, post anesthesia care unit (PACU) beds, and surgical teams (surgeons, nurses and anesthetists).Methods: Using hybrid flow shop (HFS) techniques and the break-in-moment (BIM) concept, the goal is to find a sequence that maximizes the number of procedures assigned to the ORs while minimizing the variance of intervals between surgeries' completions, smoothing the demand for downstream resources such as PACU beds and OR sanitizing teams. There are five steps to the proposed heuristic: listing of priorities, local scheduling, global scheduling, feasibility check and identification of best scheduling.Results: Our propositions were validated in a high complexity tertiary University hospital in two ways: first, applying the heuristic to historical data from five typical ST days and comparing the performance of our proposed sequences to the ones actually implemented; second, pilot testing the heuristic during ten days in the ORs, allowing a full rotation of surgical specialties. Results displayed an average increase of 37.2% in OR occupancy, allowing an average increase of 4.5 in the number of surgeries performed daily, and reducing the variance of intervals between surgeries' completions by 55.5%. A more uniform distribution of patients' arrivals at the PACU was also observed.Conclusions: Our proposed heuristic is particularly useful to plan the operation of STs in which resources are constrained, a situation that is common in hospital from developing countries. Our propositions were validated through a pilot implementation in a large hospital, contributing to the scarce literature on actual OR scheduling implementation. [ABSTRACT FROM AUTHOR]

This article describes two versions of the chance-constrained stochastic bin-packing (CCSBP) problem that consider item-to-bin allocation decisions in the context of chance constraints on the total item size within the bins. The first version is a stochastic CCSBP (SP-CCSBP) problem, which assumes that the distributions of item sizes are known. We present a two-stage stochastic mixed-integer program (SMIP) for this problem and a Dantzig–Wolfe formulation suited to a branch-and-price (B&P) algorithm. We further enhance the formulation using coefficient strengthening and reformulations based on probabilistic packs and covers. The second version is a distributionally robust CCSBP (DR-CCSBP) problem, which assumes that the distributions of item sizes are ambiguous. Based on a closed-form expression for the DR chance constraints, we approximate the DR-CCSBP problem as a mixed-integer program that has significantly fewer integer variables than the SMIP of the SP-CCSBP problem, and our proposed B&P algorithm can directly solve its Dantzig–Wolfe formulation. We also show that the approach for the DR-CCSBP problem, in addition to providing robust solutions, can obtain near-optimal solutions to the SP-CCSBP problem. We implement a series of numerical experiments based on real data in the context of surgery scheduling, and the results demonstrate that our proposed B&P algorithm is computationally more efficient than a standard branch-and-cut algorithm, and it significantly improves upon the performance of a well-known bin-packing heuristic. [ABSTRACT FROM AUTHOR]

Featured Application: A software tool for helping doctors in the automatic scheduling of elective patients is proposed. The tool has been developed considering the organizational structure of Spanish hospitals. However, it may be easily extended to any hospital (public or private) working under operation room block booking criterion. The operation room (OR) is one of the most expensive material resources in hospitals. Additionally, the demand for surgical service is increasing due to the aging population, while the number of surgical interventions performed is stagnated because of budget reasons. In this context, the importance of improving the efficiency of the surgical service is accentuated. The main objective of this work is to propose and to evaluate a Decision Support System (DSS) for helping medical staff in the automatic scheduling of elective patients, improving the efficiency of medical teams' work. First, the scheduling criteria are fixed and then the scheduling problem of elective patients is approached by a mathematical programming model. A heuristic algorithm is proposed and included in the DSS. Moreover, other different features are implemented in a software tool with a friendly user interface, called CIPLAN. Considering realistic data, a simulation comparison of the scheduling obtained using the approach presented in this paper and other similar approaches in the bibliography is shown and analyzed. On the other hand, a case study considering real data provided by the Orthopedic Surgical Department (OSD) of the "Lozano Blesa" hospital in Zaragoza (HCU) is proposed. The simulation results show that the approach presented here obtains similar occupation rates and similar confidence levels of not exceeding the available time than approaches in the bibliography. However, from the point of view of respecting the order of the patients in the waiting list, the approach in this paper obtains scheduling much more ordered. In the case of the Orthopedic Surgical Department of the "Lozano Blesa" hospital in Zaragoza, the occupation rate may be increased by 2.83%, which represents a saving of 110,000 euros per year. Moreover, medical doctors (who use this tool) consider CIPLAN as an intuitive, rapid and efficient software solution that can make easier the corresponding task. [ABSTRACT FROM AUTHOR]

Background: According to the importance of health and treatment, it is necessary to use suitable models for planning and setting surgery time. In this study, a mathematical model is offered for operational scheduling of surgeries at surgery rooms of hospitals. Material and Methods: This is an applied study and its data is related to the surgery rooms of Zahedan Al-Zahra eye hospital. Study population was the surgeries performed in March 2016. The mathematical model of scheduling surgeries at the surgery room was optimized in MATLAB2014. Results: Due to limitations on patient admission capacity in hospital surgery ward, 79 surgeries were called off. In the proposed model, the total waiting time index for performing surgeries was 1547.29, and this index was found to be 1842 without the use of the model. Therefore, the waiting time index was improved by 16%. In accordance with the third purpose (objective) function, the tally of delays for predicted surgery ending time in one month was estimated to be 69.15 hours. The process of each surgery includes four defined activities. The end time of the activities related to each surgery has been examined and it has been optimized according to the existing limitations. Conclusion: The proposed model can improve the waiting time by 16% and makes it possible to choose the surgical procedures that should be canceled and delayed according to medical priorities. [ABSTRACT FROM AUTHOR]

This paper analyzes the features of emergency surgeries under urban emergencies, and establishes a model for batch surgery scheduling, taking the issue as the flow-shop scheduling problem (FSP) with parallel machines. The modelling also takes account of the fatigue of doctors after long-term continuous operations, which causes the aging of the operating time and the success rate of saving lives. Then, the improved moth-flame optimization (IMFO) was adopted to solve the established emergency surgery scheduling model. Finally, the proposed model and algorithm were verified through an actual case. [ABSTRACT FROM AUTHOR]

Surgery scheduling is of critical importance, because an operating room (OR) is the major cost generating unit in the hospital. However, schedulers face tremendous challenges brought by case cancellation, which have been observed in most departments. On the other hand, the randomness of surgery duration also has a significant impact on an OR schedule. In this paper, we develop a stochastic integer programming model for multiple ORs that simultaneously considers the uncertainties of case cancellation and surgery duration. We aim at minimizing the costs from the perspectives of both health care providers and patients. The Benders decomposition is used to address the computational complexity. A series of experiments is conducted to show the effectiveness of the proposed model and solution approaches. A case study based on two departments at West China Hospital is carried out, where the total cost can be reduced by approximately 27%. A sensitivity analysis is conducted in the case study, from which we gain managerial insights. Note to Practitioners—The effectiveness and efficiency of operating room (OR) scheduling are highly valued by hospital practitioners, as the OR department is one of the most resource-intensive units in a hospital. The uncertainty of surgery duration and patient cancellation in OR scheduling brings a tremendous challenges to the decision makers. We propose a stochastic integer programming model by taking these uncertain factors into consideration. The proposed model significantly outperforms the current practice according to our case study. We also conduct a sensitivity analysis to obtain managerial insights. The value of stochastic solutions is calculated to show the importance of considering both the uncertain factors. [ABSTRACT FROM AUTHOR]

Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of “instance proximity” to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible. [ABSTRACT FROM AUTHOR]

Most of the current simulation models focus on implementation details without grasping the essence of the system, which makes it difficult to understand the core and the stable part of an enterprise. In this paper, we develop a Design and Engineering Methodology for Organization (DEMO)-based simulation model that combines simulation and the enterprise engineering approach in order to achieve a more holistic view of the enterprise. The four-aspect DEMO models are developed for the operating room scheduling problem, which enabled us to understand the business process from different perspectives. To develop the simulation model, an expanded DEMO with implementation model (DEMO++) is applied. The AnyLogic environment is used for execution. The model evaluates the operational performance of the case mix and master surgery plans that were developed in previous studies. The initial results show the simulation potential in the performance improvement of the operating room system. Furthermore, it makes understanding and exploring the system easier. [ABSTRACT FROM AUTHOR]

In this article, we study a surgery scheduling problem in multiple Operating Rooms (ORs) constrained by the Post-Anesthesia Care Unit (PACU) capacity within the block-booking framework. With surgery sequences predetermined in each OR, a Discrete-Event Dynamic System (DEDS) is devised for the problem. A DEDS-based stochastic optimization model is formulated in order to minimize the cost incurred from patient waiting time, OR idle time, OR blocking time, OR overtime, and PACU overtime. A sample gradient-based algorithm is proposed for the sample average approximation of our formulation. Numerical experiments suggest that the proposed method identifies near-optimal solutions and outperforms previous methods. We also show that considerable cost savings (11.8% on average) are possible in hospitals where PACU beds are a constraint. [ABSTRACT FROM PUBLISHER]

The planning of surgery durations is crucial for efficient usage of operating theaters. Both planning too long and too short durations for surgeries lead to undesirable consequences, e.g. idle time, overtime, or rescheduling of surgeries. We define these consequences as operating room inefficiency. The overall objective of planning surgery durations is to minimize expected operating room inefficiency, since surgery durations are stochastic. While most health care studies assume economically rational behavior of decision makers, experimental studies have shown that decision makers often do not act according to economic incentives. Based on insights from health care operations management, medical decision making, behavioral operations management, as well as empirical observations, we derive hypotheses that surgeons' behavior deviates from economically rational behavior. To investigate this, we undertake an experimental study where experienced surgeons are asked to plan surgeries with uncertain durations. We discover systematic deviations from optimal decision making and offer behavioral explanations for the observed biases. Our research provides new insights to tackle a major problem in hospitals, i.e. low operating room utilization going along with staff overtime. [ABSTRACT FROM AUTHOR]

In hospitals, the operating room (OR) is a particularly expensive facility and thus efficient scheduling is imperative. This can be greatly supported by using advanced methods that are discussed in the academic literature. In order to help researchers and practitioners to select new relevant articles, we classify the recent OR planning and scheduling literature into tables regarding patient type, used performance measures, decisions made, OR up- and downstream facilities, uncertainty, research methodology and testing phase. Based on these classifications, we identify trends and promising topics. Additionally, we recognize three common pitfalls that hamper the adoption of research results by stakeholders: the lack of a clear choice of authors on whether to target researchers (contributing advanced methods) or practitioners (providing managerial insights), the use of ill-fitted performance measures in models and the failure to understandably report on the hospital setting and method-related assumptions. We provide specific guidelines that help to avoid these pitfalls. First, we show how to build up an article based on the choice of the target group (i.e., researchers or practitioners). Making a clear distinction between target groups impacts the problem setting, the research task, the reported findings, and the conclusions. Second, we discuss points that need to be considered by researchers when deciding on the used performance measures. Third, we list the assumptions that need to be included in articles in order to enable readers to decide whether the presented research is relevant to them. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) in US hospitals are costly to staff, generate about 70 % of a hospital's revenues, and operate at a staffed-capacity utilization of 60-70 %. Many hospitals allocate blocks of OR time to individual or groups of surgeons as guaranteed allocation, who book surgeries one at a time in their blocks. The booking procedure frequently results in unused time between surgeries. Realizing that this presents an opportunity to improve OR utilization, hospitals manually reschedule surgery start times one or two days before each day of surgical operations. The purpose of rescheduling is to decrease OR staffing costs, which are determined by the number of concurrently staffed ORs. We formulate the rescheduling problem as a variant of the bin-packing problem with interrelated items, which are the surgeries performed by the same surgeon. We develop a lower bound (LB) construction algorithm and prove that the LB is at least (2/3) of the optimal staffing cost. A key feature of our approach is that we allow hospitals to have two shift lengths. Our analytical results form the basis of a branch-and-bound algorithm, which we test on data obtained from three hospitals. Experiments show that rescheduling saves significant staffing costs. [ABSTRACT FROM AUTHOR]

Facing an acute shortage of surgical nurses, operating room suites in China are more and more aware of the need to use surgical nurses efficiently. This goal is hard to achieve due to the strong interactions between the nurse and the surgery scheduling process. This paper addresses the benefit of integrating elective surgery and surgical nurse scheduling in terms of nurse utilization. First, an integer programming (IP) model is proposed to schedule elective surgeries and surgical nurses simultaneously. Then an efficient genetic algorithm (GA) is proposed based on the IP formulation due to the computational complexity of the integrated scheduling problem. A case study using real-life data is presented to validate the performance of the integrated approaches (the IP model and the GA) by comparing them with a two-stage approach that schedules elective surgeries and surgical nurses sequentially. [ABSTRACT FROM AUTHOR]

The high capital expenditures in surgery units fosters to hospital to look for methods to optimize the use of their resources. This article presents a GRASP algorithm to determine a good schedule for elective surgeries considering the context of a Chilean public hospital, with the objectives of maximize the priority of patients and minimize the use of overtime in the operating room. To evaluate the quality of the solution, the results are compared with a mathematical model in small instances, finding the optimum in half of the instances. For larger instances, the unscheduled patients are a 22.6% and the overtime used is a 72.8%, on average, providing a good approximation to the optimal schedule. [ABSTRACT FROM PUBLISHER]

Most surgery scheduling is done 1 day in advance. Caused by lack of overall planning, this scheduling scheme often results in unbalanced occupancy time of the operating rooms. So we put forward a rolling horizon mixed integer programming model for the scheduling. Rolling horizon scheduling refers to a scheduling scheme in which cyclic surgical requests are taken into account. Surgical requests are updated daily. The completed surgeries are eliminated, and new surgeries are added to the scheduling list. Considering day-to-day demand for surgery, we develop a non-rolling scheduling model (NRSM) and a rolling horizon scheduling model (RSM). By comparing the two, we find that the quality of surgery scheduling is significantly influenced by the variation in demand from day to day. A rolling horizon scheduling will enable a more flexible planning of the pool of surgeries that have not been scheduled into this main blocks, and hence minimize the idle time of operating rooms. The strategy of the RSM helps balance the occupancy time among operating rooms. Using surgical data from five departments of the West China Hospital (WCH), we generate surgical demands randomly to compare the NRSM and the RSM. The results show the operating rooms' average utilization rate using RSM is significantly higher than when applying NRSM. [ABSTRACT FROM AUTHOR]

Spine surgeries tend to be lengthy (mean time of 4 hours) and highly variable (with some surgeries lasting 18 hours or more). This variability along with patient preferences driving scheduling decisions resulted in both low operating room (OR) utilization and significant overtime for surgical teams at Mayo Clinic. In this paper we discuss the development of an improved scheduling approach for spine surgeries over a rolling planning horizon. First, data mining and statistical analysis was performed using a large data set to identify categories of surgeries that could be grouped together based on surgical time distributions and could be categorized at the time of case scheduling. These surgical categories are then used in a hierarchical optimization approach with the objective of maximizing a weighted combination of OR utilization and net profit. The optimization model is explored to consider trade-offs and relationships among utilization levels, financial performance, overtime allowance, and case mix. The new scheduling approach was implemented via a custom Web-based application that allowed the surgeons and schedulers to interactively identify best surgical days with patients. A pilot implementation resulted in a utilization increase of 19% and a reduction in overtime by 10%. [ABSTRACT FROM AUTHOR]

In this paper, we apply a scheduling model to address the single day surgery scheduling problem for single operating room (OR). The OR operational cost and patients' satisfaction need to be balanced. We optimize the scheduling of surgeries with two priority levels in an integrated manner, given the OR is off-duty for a fixed period. Surgeon's accumulated tiredness during working hours, and controllable surgery durations are modeled. After deriving the NP-hardness of the problem, we first solve optimally two special cases in pseudo-polynomial time, and then design a hybrid evolutionary multi-objective algorithm for the general case. Iterated local search is embedded into the elitist non-dominated sorting genetic algorithm (NSGA-II) framework, and Pareto optimal property is utilized to guide evolution towards promising areas in solution space. Finally computational studies with data from a hospital in P.R. China are performed to verify the value of algorithm hybridization against the commercial solver and original NSGA-II, and to verify the value of integrated optimization against sequential decision-making. [ABSTRACT FROM AUTHOR]

Operation theatre is one of the most significant assets in a hospital as the greatest source of revenue as well as the largest cost unit. This paper focuses on surgery scheduling optimization, which is one of the most crucial tasks in operation theatre management. A combined scheduling policy composed of three simple scheduling rules is proposed to optimize the performance of scheduling operation theatre. Based on the real-life scenarios, a simulation-based model about surgery scheduling system is built. With two optimization objectives, the response surface method is adopted to search for the optimal weight of simple rules in a combined scheduling policy in the model. Moreover, the weights configuration can be revised to cope with dispatching dynamics according to real-time change at the operation theatre. Finally, performance comparison between the proposed combined scheduling policy and tabu search algorithm indicates that the combined scheduling policy is capable of sequencing surgery appointments more efficiently. [ABSTRACT FROM AUTHOR]

Background: Operating Room (OR) is the core sector in hospital expenditure, the operation management of which involves a complete three-stage surgery flow, multiple resources, prioritization of the various surgeries, and several real-life OR constraints. As such reasonable surgery scheduling is crucial to OR management.Objective: To optimize OR management and reduce operation cost, a short-term surgery scheduling problem is proposed and defined based on the survey of the OR operation in a typical hospital in China.Method: The comprehensive operation cost is clearly defined considering both under-utilization and overutilization. A nested Ant Colony Optimization (nested-ACO) incorporated with several real-life OR constraints is proposed to solve such a combinatorial optimization problem.Results: The 10-day manual surgery schedules from a hospital in China are compared with the optimized schedules solved by the nested-ACO. Comparison results show the advantage using the nested-ACO in several measurements: OR-related time, nurse-related time, variation in resources' working time, and the end time.Conclusions: The nested-ACO considering real-life operation constraints such as the difference between first and following case, surgeries priority, and fixed nurses in pre/post-operative stage is proposed to solve the surgery scheduling optimization problem. The results clearly show the benefit of using the nested-ACO in enhancing the OR management efficiency and minimizing the comprehensive overall operation cost. [ABSTRACT FROM AUTHOR]

In this article, we propose heuristics to schedule surgeries in a German Eye hospital on a daily basis. We are interested in reducing the waiting time of the patients and in increasing the utilization of the operating rooms (OR). The sum of the overtime of the staff and the time where the ORs are not utilized for surgeries are used as a surrogate measure for OR utilization. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) that uses a random key representation for the schedules is proposed to solve heuristically this scheduling problem. The NSGA-II approach is hybridized with a local search approach. Discrete-event simulation is used to assess the fitness of the chromosomes within each generation of the NSGA-II approach taking into account the expected availability of surgeons, anesthesia doctors, nurses, stochastic surgery durations, and preferences of the patients with respect to the point of time of the surgery. The simulation model is described in detail and used to study the impact of stochastic arrival patterns of the patients and stochastic surgery durations on the performance of the executed schedules. Rescheduling strategies are investigated in different situations. We present the results of computational experiments using data from an actual eye hospital. These results clearly demonstrate that schedules obtained by dispatching rules and manually by the medical staff are outperformed by the proposed NSGA-II approach. Finally, a successful real-world implementation of the scheduling heuristic is briefly discussed. [ABSTRACT FROM PUBLISHER]

The article examines the Integrated Physician and Surgery Scheduling Problem (IPSSP) method. It presents a mathematical model that combines the commonly used constraints and objectives from physician rostering and surgery scheduling problems. The experiment reveals a relative ranking of the most influential surgery and physician constraints, and serves as a guide to assist hospital managers in discussing contract negotiations with physicians.

We consider the problem of appointment scheduling with discrete random durations but under the more realistic assumption that the duration probability distributions are not known and only a set of independent samples is available, e.g., historical data. For a given sequence of appointments (jobs, tasks), the goal is to determine the planned starting time of each appointment such that the expected total underage and overage costs due to the mismatch between allocated and realized durations is minimized. We use the convexity and subdifferential of the objective function of the appointment scheduling problem to determine bounds on the number of independent samples required to obtain a provably near-optimal solution with high probability. [ABSTRACT FROM AUTHOR]

Scheduling elective surgeries is a dynamic, sequential decision-making process that must balance the costs of deferring waiting cases and blocking higher-priority cases. Although other surgery scheduling problems have received extensive treatment in the literature, this paper presents the first single-day scheduling problem formulation to capture this aspect of the scheduling process while also incorporating surgical block schedules, block release policies, and waiting lists. Theoretical results for the special case in which all cases have the same duration motivate a range of threshold-based heuristics for the general problem with multiple case durations. Our computational results demonstrate the effectiveness of the proposed heuristics and show how block release dates affect the quality of the scheduling decisions. Based on these results, we propose a new approach to surgery scheduling. In particular, to make more equitable waiting list decisions, operating room (OR) managers should gradually release unused OR time over the course of several days leading up to the day of surgery. [ABSTRACT FROM AUTHOR]

We present a model for the surgery admission planning problem, and a meta-heuristic algorithm for solving it. The problem involves assigning operating rooms and dates to a set of elective surgeries, as well as scheduling the surgeries of each day and room. Simultaneously, a schedule is created for each surgeon to avoid double bookings. The presented algorithm uses simple Relocate and Two-Exchange neighbourhoods, governed by an iterated local search framework. The problem's search space associated with these move operators is analysed for three typical fitness surfaces, representing different compromises between patient waiting time, surgeon overtime, and waiting time for children in the morning on the day of surgery. The analysis shows that for the same problem instances, the different objectives give fitness surfaces with quite different characteristics. We present computational results for a set of benchmarks that are based on the admission planning problem in a chosen Norwegian hospital. [ABSTRACT FROM AUTHOR]

Objective This study used the discrete-events computer simulation methodology to model a large hospital surgical centre (SC), in order to analyse the impact of increases in the number of post-anaesthetic beds (PABs), of changes in surgical room scheduling strategies and of increases in surgery numbers. Methods The used inputs were: number of surgeries per day, type of surgical room scheduling, anaesthesia and surgery duration, surgical teams' specialty and number of PABs, and the main outputs were: number of surgeries per day, surgical rooms' use rate and blocking rate, surgical teams' use rate, patients' blocking rate, surgery delays (minutes) and the occurrence of postponed surgeries. Two basic strategies were implemented: in the first strategy, the number of PABs was increased under two assumptions: (a) following the scheduling plan actually used by the hospital (the ‘rigid’ scheduling – surgical rooms were previously assigned and assignments could not be changed) and (b) following a ‘flexible’ scheduling (surgical rooms, when available, could be freely used by any surgical team). In the second, the same analysis was performed, increasing the number of patients (up to the system ‘feasible maximum’) but fixing the number of PABs, in order to evaluate the impact of the number of patients over surgery delays. Conclusion It was observed that the introduction of a flexible scheduling/increase in PABs would lead to a significant improvement in the SC productivity. [ABSTRACT FROM AUTHOR]

High usage rate in a surgical suite is extremely important in meeting the increasing demand for health care services and reducing costs to improve quality of care. In this paper a goal programming model which can produce schedules that best serve the needs of the hospital, i.e., by minimizing idle time and overtime, and increasing satisfaction of surgeons, patients, and staff, is described. The approach involves sorting the requests for a particular day on the basis of block restrictions, room utilization, surgeon preferences and intensive care capabilities. The model is tested using the data obtained during field studies at Dokuz Eylul University Hospital. The model is also tested for alternative achievement functions to examine the model's ability to satisfy abstract goals. [ABSTRACT FROM AUTHOR]

In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because of this reason, we developed and implemented a strategy for the processing of heterogeneous containers in the genetic algorithm. The final product was named "genetic algorithm for scheduling optimization" (GAfSO). The results of GAfSO were tested with real data of a local hospital. Said hospital assigns different operational time to the operating rooms throughout the week. Also, the computational complexity of GAfSO is analyzed. Results show that GAfSO can assign the corresponding capacity to the operating rooms while optimizing their use. [ABSTRACT FROM AUTHOR]