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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]

This article presents a new architecture for the surgery scheduling process in hospitals, integrating Artificial Intelligence (AI) and Business Analytics (BA) techniques to optimise information flows and decision-making. The proposed platform has been designed as a modular architecture to monitor key performance indicators such as the number of surgeries scheduled, the percentage of shift occupancy, surgeries scheduled outside of deadlines and the total penalty. The AI algorithms automate the flow of information between surgeries on hold and scheduling dates, but also allow manual adjustments by healthcare professionals, guaranteeing total autonomy in user decision-making throughout the surgical scheduling process. The main aim is to reduce scheduling delays and patients on the waiting list, optimise the use of existing resources and minimise the financial penalties that are increasing every year. Through exhaustive analysis and a rigorous development process, the platform ensures a balance between the automation of processes and the indispensable analysis and human clinical intervention. [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

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.

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]

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]

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]

Complexity of surgery scheduling reduces surgical staff efficiency and patient satisfaction. Nurses and surgeons face extreme workload every day, whose schedules can be improved by correct scheduling of surgeries. This paper addresses integrated scheduling of operating rooms, nurses, and surgeons aiming to minimize makespan and maximize patient satisfaction and surgical teams' affinity. A constraint programming model and a hybrid method of NSGA-II and multi-objective dragonfly algorithm (MODA) are developed. Results indicate that high-quality solutions for problem instances with up to 150 surgeries are obtained by the CP model in less than 500 seconds. Superiority of the proposed meta-heuristic method compared to NSGA-II, MODA, SPEA-2, and a hybrid of NSGA-II and SPEA-2 is also shown. [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]

• Addresses surgery scheduling problems with considering setup time of surgical resources. • An improved artificial bee colony algorithm with combining Q-learning is proposed, named QABC. • Six local search operators are proposed according to the problem-specific knowledge. With the increasing demand for surgeries, surgery scheduling become an important problem in hospital management. Efficient surgery scheduling can enhance the optimal use of surgical resources, leading to high efficiency of surgery assignments. This work addresses surgery scheduling problems with surgical resources setup time. A mathematical model is established to describe the considered problems with the objective of minimizing the maximum completion time of the surgeries (makespan). Second, a modified artificial bee colony (ABC) algorithm is proposed, named QABC. Six local search operators are developed based on the characteristics of the problem, aiming to strengthen the local search capability of the algorithm. To further improve the performance of the algorithm, this study combines a Q-learning strategy with ABC algorithm. During each iteration of the algorithm, the Q-learning strategy is employed to guide the selection of search operators. Finally, the effectiveness of the local search operators and Q-learning based local search selection is verified by solving 20 cases with varying scales. And the results obtained by the Gurobi solver are compared with the proposed QABC. Furthermore, the proposed QABC is compared with the state-of-the-art algorithms. The experimental results and comparisons show that QABC is more effective than its peers for solving the surgery scheduling problems with setup time. [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]

This paper seeks to model and solve a surgery scheduling problem in hospitals subject to a constraint that surgeries can only be started at or after the scheduled start time, and to evaluate the effects of proposed changes to improve operating room (OR) efficiency and utilization using a Monte Carlo simulation approach. The surgery scheduling problem is to assign surgeries to ORs and assign surgery start times so as to minimize the sum of idle times of ORs and late start times for surgeries. Employing historical data provided by a large university hospital, this research provides a mathematical formulation for the problem, proposes a series of heuristics, and develops a Monte Carlo simulation model for planned surgeries to minimize the penalty. Our research demonstrates that the changes proposed can shorten the delayed start duration for surgeries and the idle times for ORs. The results can be helpful to the decision makers in hospital settings who are considering minimizing delays in surgeries start times and ORs idle times while dealing with the stochastic nature of surgery times.

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]

Abstract 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.

The high costs of operating rooms (ORs) make surgery scheduling decisions critical for hospitals, which refer to resource allocations and determining the start time of each surgery. We take into account an important issue that could affect surgery scheduling decisions, ie., assistant surgeons. The operating room scheduling problem is considered as a two-stage problem. The first stage refers to resource allocations, including allocating surgeries into ORs and assigning assistant surgeons to surgeries, while the start time of each surgery is determined in the second stage. The robustness of the model is considered in a tractable way. We develop a bound-based algorithm to solve the model with the objective of minimising the total cost in the two stages. Numerical studies show the good performances of the proposed algorithm. Sensitivity analysis is conducted to examine the effects of the key parameters. An easy-to-implement policy for decisions in the first stage is inspired by the results of the experiments.

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]

Due to the COVID-19 pandemic, hospitals are facing an overload of staff and medical equipment. To uphold the interests of surgeons, patients, and hospitals, it is crucial to allocate medical resources effectively and rationally. This study optimizes the operating room (OR) and intensive care unit (ICU) modules collaboratively, dynamically integrating their mutual constraints. The optimization considers objectives related to both patients and hospitals while also respecting surgeons' scheduling preferences and reducing their overtime. Compared with existing research that focuses on the intra-day surgery scheduling and highlight the uncertainty of the upstream (i.e., surgery duration), we emphasize the uncertainty of upstream and downstream (number of emergency patients, surgery duration and length of stay), and adopts a multi-day scheduling plan to provide practical operation scheduling for hospitals to allocate surgeons, ORs and ICU beds rationally. Due to the complexity of the model, an improved Non-dominated Sorting Genetic Algorithm-II based on fuzzy theory is proposed to solve the problem. We evaluate our method using a real case from Lyon Hospital in France and conduct an extensive sensitivity analysis on the parameters. Compared to the stochastic optimization model, our model reduces the OR costs and ICU bed by 8.33% and 4.93%, respectively. Additionally, it improves surgeon satisfaction by 38.54% and patient satisfaction by 31.90%. [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.

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.

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]

This paper addresses the operating room management problem for private hospitals aimed at the high-end customer market. A patient preference-driven policy that incorporates surgeon scheduling into the surgery scheduling process is proposed to satisfy patients' personalised preferences for surgeons and surgery dates. A stochastic programming model is formulated with the objective of minimizing operating rooms' staffing costs. A column generation-based heuristic algorithm is developed to solve the integrated surgeon and surgery scheduling issue. The performance of the algorithm is tested on different scale instances. Numerical results indicate that the patient-preference driven approach can identify an effective solution to operating room scheduling that satisfies patients' preferences with only slightly increased staffing costs. Additionally, the column generation-based heuristic algorithm can obtain solutions within a 1.6% gap of the lower bound obtained by the linear relaxed problem.

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]

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.

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.