Operating rooms have become the most important areas in hospitals because of the scarcity and cost of resources. The present study investigates Operating room scheduling and rescheduling considering the priority of surgical patients in a specialized hospital. The ultimate purpose of scheduling is to minimize patient waiting time, surgeon idle time between surgeries, and penalties for deviations from Operating room preferences. A mathematical programming model is presented to solve the problem. Because the problem is strongly NP-hard, two heuristic algorithms are presented. A heuristic algorithm based on a mathematical programming model with local search obtains near-optimal solutions for all the samples. The average relative deviation of this algorithm is 0.02%. In continuous, heuristic algorithms performance have been investigated by increasing the number of patients and reduce the number of recovery beds. Next, a rescheduling heuristic algorithm is presented to deal with real-time situations. This algorithm presents fewer changes resulting from rescheduling in comparison with the scheduling problem.

As the main source of income and expenses of hospitals, Operating rooms (ORs) are the engines of hospitals' economics and they have a significant impact on public health. Many papers concerned regarding OR planning and scheduling problems, but they have not considerably applied the simulation-based optimization approach to solve the problems. In OR scheduling problems, there are a number of ORs and some surgeons with different specialties and each surgeon has a waiting list of some patients that each surgery should be planned and scheduled on the days when relevant surgeons are available. In this study, we consider two objectives: (1) minimizing the costs of overtime staffing and ORs’ idle time, and (2) minimizing the number of waiting days for patients. The mathematical model of OR scheduling problem is developed and solved by both exact method and simulation-based optimization approach. The comparison of results obtained from exact method and simulation-based optimization approach indicates that the exact method is only able to solve the small-size problems in reasonable time, while simulation-based optimization approach find competitive solutions for both small-size and large-size problems and solve large-size problems in an acceptable time.

In a hospital، Operating rooms produces a large part of the costs، on the one hand، and a large part of the income، on the other hand. One of the most impressive ways to increase the Operating rooms efficiency is using effective ways for planning and scheduling. While this department has a close relation with other departments in the hospital، improving it’ s efficiency will raise patients satisfaction and the whole hospital efficiency and performance. In this paper، an stochastic integer mathematical model has been developed for Operating rooms planning and scheduling، based on Fugener et al model، with the aim of minimizing the cost of underutilization and overcapacity in downstream units، including intensive care unit and wards. The model aims to provide a cyclic master surgery scheduling at the tactical level based on hospital strategic decisions. The model allocates blocks to specialties based on block scheduling strategy، but also determines each surgeon’ s surgery schedule. Also، to improve the efficiency and reduce the complexity of the model for the large scale cases، the convolution stochastic distribution parts of the model have been exchanged with the expected values and variances of the capacities needed for different days of the week in a corresponding integer goal programming model. Then، different case studies have been generated by changing some of the parameters to show the objective function sensitivity to the changes. Innovation of this research compare to the Fugener et al study is providing the schedules for the surgeons، instead of the specialties. This saves time، expenses and computational operations. Also، positive half variance of the number of patients in downstream units in each day of the week has been used directly instead of the variance of the number of patients in these units.

Background and Objectives: Operating room (OR) scheduling is key to optimal Operating room productivity. The significant uncertainty associated with surgery duration renders scheduling of surgical operation a challenging task. This paper proposes a novel computational stochastic model to optimize scheduling of surgeries with uncertain durations. The model considers various surgical operation constraints in teaching hospitals, including optimal of assigning surgeons, residents, and assistant surgeons to each surgery, infection prevention constraints, availability of surgeons, and balanced distribution of operations between various groups of surgeons.Methods: A two-stage stochastic Operating room scheduling (SORS) framework was developed to minimize idle time and over time of ORs under practical constraints. The optimization model was solved using L-shaped algorithm. The performance of the SORS in proposing optimal scheduling solutions was extensively compared with that of deterministic models, as well as the performance of manual scheduling obtained from clinical data.Findings: Results from implication of model on sample real-life OR scheduling problems showed that SORS offers more efficient scheduling solutions as compared with the corresponding deterministic model. Furthermore, comparison of the SORS-proposed schedules with the practical schedules indicated that SORS can remarkably reduce the Operating room idle times (96%) and overtimes (87%), suggesting the utility of this model in clinical practice.Conclusions: A novel validated computational Operating room scheduling model was developed, which can potentially be employed to achieve higher Operating room performance.

Objective: Hospitals are one of the most important elements of the health sector, which is the most important and costly part of them are Operating rooms. Therefore, locating and allocating resources appropriately and appropriate scheduling for the Operating room, can greatly solve the health problems of the community. In this article, an integrated model for locating-allocating and scheduling the Operating rooms to build facilities at the appropriate places, allocating patients to facilities and Operating rooms and scheduling Operating rooms with the goals of reducing costs and reducing the overtime of the Operating rooms, is presented. Also, surgeons have the maximum working time per day, and then their performance is faced with an error. Methods: Given the NP-hard of the model and the inability of accurate methods for solving large-scale problems, a metaheuristic method based on the genetic algorithm has been developed for the problem and its efficiency has been studied in a wide range of samples examples. Results: Computational results showed that the proposed algorithms can effectively help to generate reasonable responses. Conclusion: The results of the problem indicate the importance of locating and integrating scheduling of facilities in reducing the waiting time of patients and their distance from facilities. The proposed algorithm is also capable of solving large-scale problems at an efficient response.

Operating rooms (ORs) are among the most influential departments of a hospital that a major portion of its expenditures and revenues originate from it. Due to the limited resources and the presence of different stockholders, effective management, and the optimal planning of this department are challenging. This paper develops a mathematical model to address the integrated problem of the Operating room and surgical member scheduling with the objective of makespan minimization. In the proposed model, several aspects, including the availability and necessity of surgical members and equipment, and lunch break consideration, are incorporated. Finally, a case study related to an OR department in a general hospital is provided to assess the applicability and performance of the proposed model.

Given the increasing human need for health systems and the costs of using such systems, the problem of optimizing health-related systems has attracted the attention of many researchers. One of the most critical cases in this area is the Operating room scheduling. Much of the cost of health systems is related to Operating room costs. Therefore, planning and scheduling of Operating rooms can play an essential role in increasing the efficiency of health systems as well as reducing costs. Given the uncertain factors involved in such matters, attention to uncertainty in this problem is one of the most critical factors in the results. In this study, the problem of the daily scheduling of the Operating room with uncertain surgical time was investigated. For minimizing overhead costs and maximizing the number of surgeries to reduce patients' waiting time, after introducing a mathematical model, a chance-constrained programming approach is used to deal with its uncertainty. In this study, also, a harmony search algorithm is proposed to solve the model because of its NP-Hardness. By performing the numerical analysis and comparing the presented algorithm result with a genetic algorithm, the results show that the proposed algorithm has a better performance.

Introduction: Self-efficacy is the belief in one's ability and ability to perform the task. One way to enhance employees' job self-efficacy is the manager's leadership style. The purpose of this study was to determine the correlation between leadership style of Operating room supervisors and Operating room technologists' selfefficacy in teaching hospitals of Iran University of Medical Sciences in 2017-2018. Methods: The present study is a descriptive-correlational study which was performed on 220 Operating room technologists of hospitals of Iran University of Medical Sciences by stratified random sampling. Data were collected using two questionnaires: "Multifactor Leadership Questionnaire" and "Job Self-Efficacy Questionnaire. " The content validity of the questionnaires was confirmed in previous studies, and their reliability was tested by the retest method. Data were analyzed using SPSS 21 software. Results: Most Operating room supervisors (65. 5%) had a transformational leadership style. The majority of Operating room technologists (71. 4%) had high self-efficacy. There was a direct and significant correlation between transformational, exchangeable, and non-interference leadership styles of Operating room supervisors with occupational self-efficacy (P < 0. 05). Also, the mean score of self-efficacy in transformational and exchangeable leadership styles was higher than non-interventionist leadership styles. Conclusions: There is a direct relationship between job self-efficacy with all three types of leadership styles, but the mean score of self-efficacy in transformational and exchangeable leadership styles is higher than the non-intervention style. Therefore, it is recommended that supervisors use transformational or transactional leadership styles, or a combination of these two leadership styles, depending on the situation in the Operating room.