Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

Day Surgery Scheduling and Optimization in Large Public Hospitals in China: A Three-Station Job Shop Scheduling Problem

Day surgery scheduling allocates hospital resources to day surgical cases and decides on the time to perform the surgeries in the day surgery center (DSC). Based on the day surgery service process of large public hospitals in China, we found that the service efficiency of the process depends on the utilization of hospital resources efficiently and could be optimized through day surgery scheduling. We described it as a flexible flow shop owing to the three-station nature of surgery. Allocating all types of hospital resources to the three stations and determining the length of time for each stage during surgery are crucial to improving the efficiency of DSC. This paper integrates a three-station job shop scheduling problem (JSSP) into the day surgery scheduling and optimization problem. The JSSP was formulated as a mixed-integer linear programming model, and the elicitation of the model for scheduling surgeries with different priorities in the DSC is discussed. The model illustrated a case study of the DSC within West China Hospital (WCH). Numerical experiments based on the genetic algorithm design were conducted. Compared to the other optimization strategies, we proposed that the three-station job shop scheduling strategy (TSJS) could not only improve the efficiency and reduce the waiting time of the patients of the DSC in large public hospitals in China but also allow for timely scheduling adjustments during the advent of emergency surgeries.

A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem

In this paper, a new hybrid AHP and Dempster—Shafer theory of evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, a three-phase framework is proposed. In the first phase, quantitative research was conducted to identify the risk factors that can influence a project. Then, a hybrid PCA-agglomerative unsupervised machine learning algorithm is proposed to classify the projects in terms of Properties, Operational and Technological, Financial, and Strategic risk factors. In the third step, a hybrid AHP and Dempster—Shafer theory of evidence is presented to select the best alternative with the lowest level of overall risks. As a result, four groups of risk factors, including Properties, Operational and Technological, Financial, and Strategic risk factors, are considered. Afterward, using an L2^4 Taguchi method, several experiments with various dimensions have been designed which are then solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index, Reduced Risk Indicator, and Solving Time. The findings indicated that, compared to classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.

Healthcare scheduling in optimization context: a review

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.

Air and ground ambulance location-allocation-routing problem for designing a temporary emergency management system after a disaster

Disasters cause a huge number of injured patients in a short time while existing emergency facilities encountered devastation and cannot respond properly. Here, the importance of implementing temporary emergency management becomes clear. This study aims to locate some temporary emergency stations across the area by maximal covering after a disaster. Furthermore, a multi-mode fleet is used for transferring patients using different modes of transportation (e.g. helicopter ambulance and bus ambulance). Since the type of patients may change over periods, medical servers can displace among temporary emergency stations dynamically according to disaster severity. For this purpose, a new bi-objective dynamic location-helicopter ambulance allocation-ambulance routing model with multi-medical servers is presented. The first objective function minimizes the operational costs related to the newly designed Emergency Medical Service along with the rate of human loss. The second objective function minimizes the critical time spent before the medical treatment. To validate the developed model, the augmented ε-constraint method is used and applied for the Tehran city, which shows the applicability of the model. Finally, two meta-heuristic algorithms are customized for large-sized problems, and the related results are compared based on multi-objective algorithms' performance comparison metrics to find the more efficient one.

A multi-objective model for a nurse scheduling problem by emphasizing human factors

Assigning nurses to appropriate departments and work shifts based on human factors can strengthen teamwork and boost the efficiency of healthcare systems. The human factors considered in this study include skill, preference, and compatibility of nurses. In this regard, a unique multi-objective mathematical model for nurse scheduling is proposed in this article, in which nurses' decision-making styles are taken into account. Three objectives, including minimization of the total cost of staffing, minimization of the sum of incompatibility among nurses' decision-making styles assigned to the same shift days, and maximization of the overall satisfaction of nurses for their assigned shifts, are addressed in this model. Three meta-heuristics, namely, multi-objective Keshtel algorithm, non-dominated sorting genetic algorithm II, and multi-objective tabu search, are developed to solve the problem. Moreover, a data envelopment analysis method is employed to rank the obtained Pareto solutions. Afterwards, a real-life case at a large hospital in Tehran, Iran, is investigated. Eventually, the applicability and effectiveness of the proposed model are assessed based on the experimental results.