Rostering Research Articles

Self-scheduling for nurse: A concept analysis

Quality nursing care must be supported by appropriate nurse scheduling arrangements. Inappropriate nursing scheduling harms nurses’ work. Clarification of terms is necessary to inform nurses and service managers, promote consistent theoretical and operational definitions in research, and develop prevention strategies to provide quality nursing care. This research aims to clarify the meaning of the concept of self-scheduling. The design used is concept analysis. Data sources were searched from electronic databases (Sciendirect, Google Schoolar, Taylor & Francis, SpringerLink, and IEEE). The Walker & Avant method was applied to guide the concept analysis to identify and define attributes, antecedents, and consequences. Self-scheduling has seven attributes: service schedule, flexibility, autonomy, demands, conditions and capabilities, and responsibility. The antecedents include the number of staff, competence, self-efficacy, motivation, work experience, education, age, and career ladder. Proper self-scheduling has positive effects, such as on job satisfaction, work safety, patient safety, and work‒life balance. The negative consequences of self-scheduling include workload, fatigue and stress. The conclusion is that self-scheduling is very important, and the implementation of self-scheduling on nurses' service schedules that are not quite right contributes to shift work and patient safety.

Doctor and Nurse Scheduling in Emergency Room Using Firefly Algorithm

The Emergency Room (ER) is a part of the hospital responsible for providing initial treatment to patients with life-threatening conditions. The operational hours of the ER follow the schedule set by the hospital. ER must be ready to serve emergency patients 24 hours a day and 7 days a week. Therefore, the scheduling of doctors and nurses in the ER needs to be well-managed to enhance the efficiency of doctors and nurses in responding emergency patients quickly and effectively. In this study, the problem of doctors and nurses scheduling in the ER is solved using the Firefly Algorithm, in which doctors and nurses represented as fireflies. This algorithm is chosen since its ability to find optimal solutions for complex optimization problems. In this research, doctors and nurses can submit schedule requests to improve job satisfaction. The optimization model is constructed by a number of constraints including the availability of doctors and nurses, schedule requests, and the operational needs of the ER. The Firefly Algorithm is applied to find the optimal solution for the model. Simulation results show that this algorithm can produce an optimal schedule, in which 70.6% of doctors' schedule requests and 98.2% of nurses' schedule requests are being fulfilled.

Intelligent Clinic Nurse Scheduling Considering Nurses Paired with Doctors and Preference of Nurses.

The nurse scheduling problem (NSP) has been a crucial and challenging research issue for hospitals, especially considering the serious deterioration in nursing shortages in recent years owing to long working hours, considerable work pressure, and irregular lifestyle, which are important in the service industry. This study investigates the NSP that aims to maximize nurse satisfaction with the generated schedule subject to government laws, internal regulations of hospitals, doctor-nurse pairing rules, shift and day off preferences of nurses, etc. The computational experiment results show that our proposed hybrid metaheuristic outperforms other metaheuristics and manual scheduling in terms of both computation time and solution quality. The presented solution procedure is implemented in a real-world clinic, which is used as a case study. The developed scheduling technique reduced the time spent on scheduling by 93% and increased the satisfaction of the schedule by 21%, which further enhanced the operating efficiency and service quality.

Optimization Automates Emergency Department Nurse Scheduling at Hartford Hospital

A collaboration between Hartford Hospital, MIT, and Dynamic Ideas LLC optimizes nurse staffing in emergency department using a two-phase optimization approach. We developed and implemented robust optimization models and automated scheduling software, reducing staffing costs by 5%–8%, enhancing patient care coverage by 8%–25%. This streamlined process is projected to save approximately $720,000 annually, replacing a labor-intensive scheduling method previously in place.

The home health care routing with heterogeneous electric vehicles and synchronization

AbstractThis paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

The Scheduling Mode of Anesthesia Nurses Affects Postanesthesia Care Unit Efficiency: A Single-Center Retrospective Study From China

PurposeAnesthesia nurses play an important post-surgical role during anesthesia recovery period, which is characterized by a high incidence of complications related to anesthesia and surgery. Strengthening staff allocation and skill management in LOS (PACU) is therefore particularly important. We aim to investigate the effect of two schedule modes for anesthesia nurses on PACU efficiency. DesignA retrospective observational cohort study. MethodWe conducted a retrospective study in a large tertiary academic medical center. In 2018, the PACU operated with traditional scheduling and the nurse-to-patient ratio was 1.2:1. The PACU implemented intensive scheduling and this ratio was adjusted to 1:1 in 2019 by adjusting the anesthesia nurse allocation scheme. We compared the number of admitted patients, length of PACU stay, the incidence of anesthesia-related complications, and nurse satisfaction with the two modes. FindingsThe total number of admitted patients was 10,531 in 2018 and 10,914 in 2019. PACU admitted 401 more patients in 2019 than in 2018, even with 2 fewer nurses per day. Nevertheless, the median length of PACU stay in 2019 was statistically significantly shorter than in 2018 [29 (22–40) vs. 28 (21–39), P＜0.001], while the incidence of anesthesia-related complications including postoperative pain, nausea and vomiting, hypertension, and shivering were comparable in the two years (P > 0.091). The intensive scheduling implemented in 2019 received more satisfaction from nurses than the traditional scheduling applied in 2018 (P < 0.01). ConclusionThe scheduling of anesthesia nurses affects PACU efficiency. The intensive scheduling mode implemented in 2019 resulted in a comparable number of admitted patients, a better quality of care, and higher nurse satisfaction than those under the traditional scheduling mode.

Nurse rostering: understanding the current shift work scheduling processes, benefits, limitations, and potential fatigue risks

BackgroundImplementing appropriate shift work schedules can help mitigate the risk of sleep impairment and reduce fatigue of healthcare workers, reducing occupational health and safety risks. In Australia, the organisation has a responsibility to make sure all reasonable measures are taken to reduce fatigue of staff. Therefore, it is important to assess what the current rostering processes is for staff responsible for creating the rosters for nurses.AimThe aims of the project were to understand (1) who creates the rosters and what the process is, (2) what training and knowledge these staff have in establishing rostering schedules that optimise the sleep and wellbeing of staff, and (3) what the benefits and limitations are of current rostering practices.MethodsFindings were generated through semi-structured interviews, using cluster coding to form categories. Twenty four nurses responsible for rostering staff were interviewed from three different sites in Victoria (one metropolitan and two regional/rural hospitals). Data was analysed using selected grounded theory methods with thematic analysis.ResultsThe common themes that came out of the interviews were that rostering staff were under prepared, unaware of fatigue and safety guidelines and polices from governing bodies and had not received any education or training before taking on the role. The most common rostering style was self-rostering, where staff could submit preferences. However, there were concerns about staff fatigue but were divided as to who should be responsible, with many saying it was up to staff to preference shifts that they could cope with. The final theme was cultural barriers to change.ConclusionWhile self-rostering resulted in staff having more freedom and flexibility, shift preferences may be influenced more so by a need to fit with lifestyle rather than to minimise fatigue and increase safety in the workplace. Greater consideration of the impact of shift work schedules on fatigue is required to ensure that the layers of clinical governance in health care organisations minimise the risk of occupation health and safety issues for employees delivering direct patient care.

Optimizing Nurse Scheduling: A Supply Chain Approach for Healthcare Institutions

When managing an organization, planners often encounter numerous challenging scenarios. In such instances, relying solely on intuition or managerial experience may not suffice, necessitating a quantitative approach. This demand is further accentuated in the era of big data, where the sheer scale and complexity of constraints pose significant challenges. Therefore, the aim of this study is to provide a foundational framework for addressing personnel scheduling, a critical issue in organizational management. Specifically, we focus on optimizing shift assignments for staff, a task fraught with complexities due to factors such as contractual obligations and mandated rest periods. Moreover, the current landscape is characterized by frequent employee shortages across various industries, with many organizations lacking efficient and dependable management tools to address them. Therefore, our attention is particularly drawn to the nurse rostering problem, a personnel scheduling challenge prevalent in healthcare settings. These issues are characterized by a multitude of variables, given that a single healthcare facility may employ hundreds of nurses, alongside stringent constraints such as the need for adequate staffing levels and rest period’s—post-night shifts. Furthermore, the ongoing COVID-19 pandemic has exacerbated staffing challenges in healthcare institutions, underlining the importance of accurately assessing staffing needs and optimizing shift allocations for effective operation amidst crisis situations.

Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem

The Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic algorithms, such as adaptive neighborhood search (ANS) and variable neighborhood descent (VND), mainly focus on optimizing the current solution without considering potential long-term consequences, which may easily get stuck in local optima and limit the overall performance. Thus, we propose a multi-agent deep Q-network-based metaheuristic algorithm (MDQN-MA) for NRP to harness the strengths of various metaheuristics. Each agent encapsulates a metaheuristic algorithm, where its available actions represent different perspectives of the problem environment. By combining their strengths and various perspectives, these agents can work collaboratively to navigate and search for a broader range of potential solutions effectively. Furthermore, to improve the performance of an individual agent, we model its neighborhood search as a Markov Decision Process model and integrate a deep Q-network to consider long-term impacts for its neighborhood sequential decision-making. The experimental results clearly show that an individual agent in MDQN-MA can outperform ANS and VND, and multiple agents in MDQN-MA even perform better, achieving the best results among metaheuristic algorithms on the Second International Nurse Rostering Competition dataset.

Ethical decision-making in human-automation collaboration: a case study of the nurse rostering problem

AbstractAs artificial intelligence (AI) is increasingly present in different aspects of society and its harmful impacts are more visible, concrete methods to help design ethical AI systems and limit currently encountered risks must be developed. Taking the example of a well-known Operations Research problem, the Nurse Rostering Problem (NRP), this paper presents a way to help close the gap between abstract principles and on-the-ground applications with two different steps. We first propose a normative step that uses dedicated scientific knowledge to provide new rules for an NRP model, with the aim of improving nurses’ well-being. However, this step alone may be insufficient to comprehensively deal with all key ethical issues, particularly autonomy and explicability. Therefore, as a complementary second step, we introduce an interactive process that integrates a human decision-maker in the loop and allows practical ethics to be applied. Using input from stakeholders to enrich a mathematical model may help compensate for flaws in automated tools.

AN APPLICATION OF GRAPH COLORING: NURSE SCHEDULING PROBLEM

The Nurse Rostering Problem (NRP), also known as the Nurse Scheduling Problem (NSP), represents a challenging scheduling issue that significantly impacts hospital staff worldwide on a daily basis. Nursing staff scheduling is one of the most important but complex problem for the hospital management. NSP is a critical subclass of scheduling problems known for their complexity. This paper introduces an efficient approach to solving the Nurse Scheduling Problem (NSP), aiming to meet the needs of nurses, patients, and hospital management. It requires critical thinking and good decision-making skills to develop a roster that effectively balances patient care, employee and other needs in the organization. To reduce the problem's complexity, graph coloring techniques have been utilized.

Constructing modified variable neighborhood search approaches to solve a nurse scheduling problem

This study proposed multiple revised variable neighborhood search (VNS) approaches applying the greedy concept to solve a nurse scheduling problem (NSP). In this paper, we developed three greedy-neighbourhood-swapping mechanisms (greedy-2-exchange, greedy-3-exchange, and greedy-4-exchange) to conduct local searches based on one-, two-, or three-neighbourhood structures that accounted for constraints imposed by government and hospital regulations. The greedy-neighbourhood-swapping mechanisms were used to identify medical staff members with the highest soft-constraint (e.g. nurses’ preferences) violation weights on a given day who then swapped their shifts with others. To validate the proposed VNS approaches, we also conducted a case study. Based on the testing instances, all of the proposed VNS approaches generated optimal or near-optimal solutions, and the differences between them were small. The optimal number of the neighbourhood structures was determined to be two, confirming that a larger number of neighbourhoods in a neighbourhood structure would not necessarily be associated with more easily escaping local optima. Furthermore, the resulting outcomes supported the conclusion that the proposed modified VNS approaches generated better schedules for the medical staff members of hospitals than the compared meta-heuristic algorithms.

A Dedicated Pricing Algorithm to Solve a Large Family of Nurse Scheduling Problems with Branch-and-Price

In this paper, we describe a branch-and-price algorithm for the personalized nurse scheduling problem. The variants that appear in the literature involve a large number of constraints that can be hard or soft, meaning that they can be violated at the price of a penalty. We capture the diversity of the constraints on individual schedules by seven generic constraints characterized by lower and upper bounds on a given quantity. The core of the column generation procedure is in the identification of individual schedules with minimum reduced cost. For this, we solve a shortest path problem with resource constraints (SPPRC) where several generic constraints are modeled as resource constraints. We then describe dominance rules adapted to the presence of both upper and lower bounds on the resources and leverage soft constraints to improve the dominance. We also describe several acceleration techniques for the solution of the SPPRC, and branching rules that fit the specificities of the problem. Our numerical experiments are based on the instances of three benchmarks of the literature including those of the two international nurse rostering competitions (INRC-I and INRC-II). Their objective is threefold: assess the dominance rules and the acceleration techniques, investigate the capacity of the algorithm to find provable optimal solutions of instances that are still open, and conduct a comparison with best published results. The most noticeable conclusion is that the improved solution of the SPPRC allows to solve optimally all the INRC-II instances where a four-week planning horizon is considered and 40% of the eight-week instances. History: Accepted by Andrea Lodi, Area Editor for Design &amp; Analysis of Algorithms–Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.0019 .

Nurse Scheduling Problem in Pandemic Situation: An Application in Ankara City Hospital

With the Covid-19 epidemic, which first appeared in China in December 2019 and affected the world in a short time, the importance of health is increasing day by day. The increasing importance of health in combating the epidemic causes an increase in the need for hospitals and health personnels. The health personnels that have the most role in this struggle are nurses. As nurses are mentally and physically healthy, it is possible to provide better service to patients. In this study, a goal programming model has been developed in order to make November 2020 shift schedule of 50 nurses working in 4 clinics in Ankara City Hospital's General Hospital building. While developing the model; hospital rules, laws, Covid-19 measures were taken into consideration. In addition, extra precautions have been taken for the special conditions of nurses and for nurses who are in the high risk group for the trans-mission of the virus. The optimal result has been achieved by writing the developed model into the ILOG CPLEX Studio IDE program.

Solving Nurse Rostering Optimization Problem using Reinforcement Learning - Simulated Annealing with Reheating Hyper-heuristics Algorithm

Nurse rostering is the process of scheduling hospital nurses for a certain period of time. This is a complex and interesting task that involves multiple perspectives, including those of the hospital, nurses, and patients. In addition, nurse scheduling must consider existing constraints to obtain a schedule that is close to optimal. Common constraints include the number of available nurses, their abilities, labor regulations, and hospital policies. The numerous constraints in nurse scheduling lead to a problem known as the Nurse Rostering Problem (NRP), which is difficult to solve conventionally and is considered an NP-hard problem. Many researchers have attempted to solve NRP computationally, as this approach reduces the time required to create nurse schedules. The less time needed to create nurse schedules, the more time can be allocated to improving medical services, which ultimately impacts the quality of healthcare in the hospital's surrounding area. Several approaches have been used to solve NRP, including hyper-heuristic methods, which are search methods used to solve optimization problems. The aim of this study is to employ a hyper-heuristic approach using Reinforcement Learning - Simulated Annealing with Reheating algorithm to solve the nurse scheduling problem in hospitals in Norway. The results show that the Reinforcement Learning - Simulated Annealing with Reheating algorithm can produce schedules with an 82% improvement in solutions. This algorithm also outperforms the comparison algorithms, with a 3% to 47% improvement over the Simple Random - Hill Climbing algorithm, a 2% to 12% improvement over the Reinforcement Learning - Hill Climbing algorithm, and a 0.7% to 7% improvement over the Reinforcement Learning - Simulated Annealing algorithm.

Nurse scheduling problem: Investigating the principles of operators in evolutionary algorithm for small size population

Developing an effective nurse shifts assignment system, that considers diverse nurse preferences and fairness as well as ward coverage in practical operational scenarios, is a complex and time-consuming task. Failing to address various constraints with different levels of precedence can lead to undesirable nurse schedules. The efficiency of such a system relies heavily on the attributes of an automated scheduling approach or the proficiency of a head nurse. Therefore, this paper investigates the principles of designing artificial computing operators for a matrix representation solution in the evolutionary algorithm hybridization. Several parent selections, each with different selection intensities that prioritize elitism and dissimilarity characteristics, are reviewed. Additionally, the integration of parent selection intensities with specific fragment sizes of crossovers are studied when designing a well-performing algorithm. The evaluation criteria encompass algorithm reliability, accuracy, effectiveness, and efficiency. The study reveals that the modified Maximax and Maximin parent selection with Block-wise crossover achieved a higher quality schedule with the lowest fitness value. In conclusion, a small-sized population proves suitable for addressing the complex computational problem that consist of heavy constraints. The selection intensity should strike a balance between elitism and dissimilarity intensities when combined with a smaller fragment size of mating strategy.

A Framework for Nurse Scheduling Problems Based on Prevent Bad-Relationships

A Framework for Nurse Scheduling Problems Based on Prevent Bad-Relationships

A Dynamic Staffing & Scheduling Solution: The Build and Implementation of a Logistics Engine to Optimize Nurse Schedules and Rosters

The use of electronic scheduling and staffing tools should be standard practice in health care. Gone are the days of paper scheduling, replaced with mobile apps and sleek desktop applications. The frontline clinician has access to scheduling, paid time off (PTO), trades, and timecard management in the palm of their hand. Ease of use and work flexibility are top of mind for many frontline clinicians and leaders. Yet even with the newest technology and sleekest design, there continues to be a gap in functionality and practice: predictive scheduling. The tools facilitate the management of actions such as balancing a schedule, automating trades, simple approval of time off, and clean record keeping, but have limited decision support functionality to accurately predict how many nurses should be scheduled to match projected demand, or how many nurses should be hired to meet the schedules modeled to match that projected demand. Therefore, nurse managers and leaders spend hours trying to figure out how many nurses are needed on a schedule that would require as little rebalancing as possible for the actual shift in hopes of limiting the chance of cancelations or needing to expensively recruit for unfilled shifts with incentive pay or overtime. The need for decision support tools for predictive scheduling is paramount and critical for effective labor management. To overcome the gap in technology and capability, St. Luke’s Health System designed, built, and implemented their own logistics engine to move from reactive nurse staffing to proactive nurse scheduling, reaping the rewards of managerial time saved, controlled labor cost, a more predictable roster, and a more satisfied nursing workforce.

An integrated Lexicographic goal programming and dynamic satisfaction function model for effective nurse scheduling

The nurse-scheduling is a common problem in hospitals. The problem requires a timetable considering hospital regulations and nurses’ demands and preferences. This study proposes an integrated Lexicographic goal programming and dynamic satisfaction function model for solving nurse-scheduling problems. The proposed model provides the head nurses with a balanced and fair automated schedule. The model considers two aspects related to hospital regulation constraints. First, the dynamic aspect allows the decision-makers to integrate their judgment to the changing circumstances caused by nurse absenteeism. Second, the satisfaction aspect considers the decision maker’s preferences in assigning nurses’ surplus or shortages to the shifts. The model’s primary goal is to give nurses a fair and balanced schedule with an evenly distributed workload. A fair and balanced schedule helps maintain nurses’ welfare and provide quality patient service. We present a case study in an intensive care unit to demonstrate the effectiveness of our approach compared to the conventional goal programming method.

A Novel Approach for Flexible Nurse Rostering Allotment Using the Nature Inspiration Bee Colony Optimization (BCO) Algorithm

A Novel Approach for Flexible Nurse Rostering Allotment Using the Nature Inspiration Bee Colony Optimization (BCO) Algorithm