Rostering Research Articles

Poor dietary behaviors among hospital nurses in Seoul, South Korea

BackgroundNurses reportedly practice unhealthy behaviors due to unfavorable work schedules. Korean nurses are particularly vulnerable to dietary and health behaviors due to high patient-to-nurse ratios; however, there are few studies on Korean hospital nurses' health behaviors. PurposeTo investigate the dietary and health behaviors of Korean hospital nurses according to their work schedule type. MethodThis was a cross-sectional descriptive study using survey data from 340 hospital nurses. Nurses' dietary and health behaviors were evaluated across different work schedules and compared to the general Korean female population. ResultsNurses with rotating night shift schedules were more often underweight than nurses without night shifts and had more unhealthy dietary behaviors, such as skipping breakfast and eating late night snacks. Nonetheless, Korean nurses practiced healthy behaviors, such as engaging actively in physical activity. ConclusionsHospitals should create policies to provide healthy schedules for nurses to mitigate the negative effects of rotating and night shifts. However, these management-led measures will be effective only if individual nurses realize and take responsibility for their health behaviors and choices.

Hybridization of harmony search with hill climbing for highly constrained nurse rostering problem

This paper proposes a hybrid harmony search algorithm (HHSA) for solving the highly constrained nurse rostering problem (NRP). The NRP is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses; each has specific skills and work contract, to a predefined rostering period according to a set of constraints. The harmony search is a metaheuristic approach, where the metaheuristics are the most successful methods for tackling this problem. In HHSA, the harmony search algorithm is hybridized with the hill climbing optimizer to empower its exploitation capability. Furthermore, the memory consideration operator of the HHSA is modified by replacing the random selection scheme with the global-best concept of particle swarm optimization to accelerate its convergence rate. The standard dataset published in the first international nurse rostering competition 2010 (INRC2010) was utilized to evaluate the proposed HHSA. Several convergence scenarios have been employed to study the effects of the two HHSA modifications. Finally, a comparative evaluation against twelve other methods that worked on the INRC2010 dataset is carried out. The experimental results show that the proposed method achieved five new best results, and 33 best published results out of 69 instances as achieved by other comparative methods.

Memetic Algorithm for the Nurse Scheduling Problem

The Nurse Scheduling Problem (NSP), like the well-known Travelling Salesman Problem (TSP), is an NP-hard problem. In this study, we use a tailor-made meta-heuristic Memetic Algorithm (MA) to optimize the NSP. The MAis a hybrid algorithm, being a combination of the Genetic Algorithm (GA) and a local search algorithm. The performance of the MA is found to be superior to that of a solitary algorithm like GA. The MA solves the NSP in two stages. In the first stage, the randomly generated solutions are evolved till they become feasible (i.e., the hard constraints are satisfied) and in the second stage, these solutions are further evolved so as to minimize the violations of the soft constraints. In the final stage, the MA produces optimal solutions in which the hard as well as the soft constraints are completely satisfied.

A hybrid artificial bee colony for a nurse rostering problem

The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem. This paper proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empower its exploitation capability and the usage of HCO is controlled by hill climbing rate (HCR) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset.

Fuzzy Mathematical Modeling Approach for the Nurse Scheduling Problem: A Case Study

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign the shifts to the nurses in order to satisfy the demands of each day during the planning horizon. In this study, we consider the NSP in the largest hospital in Iran, i.e., Milad. A mathematical programming model is proposed to maximize the nurses’ preferences to work in their favorable shifts as well as to minimize the total surplus nurses to cover the demands of each day. The schedule of the nurses on the last days of the previous planning horizon is considered for assigning the shifts to the nurses on the beginning days of the current planning horizon. Moreover, the leave days requested by the nurses are taken into consideration. Considering the uncertainties in the real-world problems has a great effect on providing the higher quality schedules. In this study, the uncertainty is considered in the nurses’ preferences and the number of surplus nurses. To treat the uncertainties in the research problem, four different types of the fuzzy solution approaches are applied. Then, the fuzzy models are formulated based on the proposed fuzzy solution approaches to provide a more flexible solution for policy makers.

Nurse Scheduling with Joint Normalized Shift and Day-Off Preference Satisfaction Using a Genetic Algorithm with Immigrant Scheme

To make a fair and satisfactory nurse shift schedule, this paper proposes a novel preference satisfaction function, in which numbers of the preferred work shifts and days-off of the nursing staff are balanced, and ranks for preferences and number of the preference ranks satisfied so far are also considered. Such a preference function is capable of equivalently and fairly planning the nurse preference schedule to improve the total satisfaction. Additionally, distributed sensors can be applied to collect the information on hospital beds to provide the schedule planner to determine the lowest required amount of manpower for each work shift, to avoid the working overload of the nursing staff. To solve the nursing schedule problem, we propose a genetic algorithm (GA) with an immigrant scheme, in which utilization of the immigrant scheme is helpful in efficiently reducing amount of infeasible solutions due to practical scheduling constraints, so that the GA can efficiently find better solutions for larger-scale problems. Performance of the proposed GA with and without solution recovery scheme is evaluated by conducting a comprehensive experimental analysis on three different-size instances.

The association between rotating shift work and increased occupational stress in nurses.

The aim of this study was to investigate whether rotating shift work increases occupational stress in nurses. This study measured shift work scheduling and occupational stress by using the Effort-Reward Imbalance model with self-reported questionnaires in a sample of 654 female nurses. Overcommitment risk was higher in nurses who worked rotating shifts than in those who worked day/non-night shifts (OR, 2.16; 95% CI, 1.03-4.66). However, an effort/reward imbalance was not directly associated with work schedules (OR, 1.88; 95% CI, 0.87-4.35). Among nurses working rotation rotating shifts, those who had 2 days off after their most recent night shifts showed an alleviated risk of overcommitment (OR, 0.52; 95% CI, 0.32-0.82), but those who had worked for at least one series of 7 consecutive work days per month had an increased risk of effort/reward imbalance (OR, 2.75; 95% CI, 1.69-4.48). Additionally, those who had little or no participation in planning working hours and shift scheduling and worked overtime at least three times per week during the preceding 2 months tended to have high stress. The nurses who worked rotating shifts tended to experience work-related stress, but their stress levels improved if they had at least 2 days off after their most recent night shift and if they were not scheduled to work 7 consecutive days. These empirical data can be used to optimize work schedules for nurses to alleviate work stress.

Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital’s demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses’ preferences for working shifts and weekends off by considering several important factors such as hospital’s policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Α two-phase adaptive variable neighborhood approach for nurse rostering

This contribution presents a two-phase variable neighborhood search algorithm for solving nurse rostering problems. In order to demonstrate the efficiency of the proposed algorithm, it is firstly applied to all nurse rostering problem instances as proposed in the First International Nurse Rostering Competition (INRC-2010). Computational results assessed on all three sets of sixty competition instances demonstrate that the proposed algorithm improves the best known results for two instances, inside the time limits of the competition, while achieving the best known bounds for forty eight other instances. The proposed algorithm was also applied to seven other nurse rostering instances reported in the respective literature and managed to achieve the best known result in six of them while improving the best known result in one instance. The proposed algorithm, as well as its differences from existing approaches are presented, described and discussed in detail.

A Two-Stage Stochastic Programming for Nurse Scheduling in Razavi Hospital

Background: One of most important operations research problems is Nurse Scheduling Problem (NSP) that tries to find an optimal way to assign nurses to shifts with a set of hard constraints. Most of the researches are dealing with this problem in deterministic environment with constant parameters. While In the real world applications of NSP, the stochastic nature of some parameters like number of arriving patients, stay periods, etc. are some sources of uncertainties that need to be controlled to provide a qualified schedule. Objectives: In this article we propose our model in an uncertain environment in Department of Heart Surgery in Razavi Hospital. Materials and Methods: The demand and stay period of patients are stochastic whose the distribution is determined from historical data. Finally, the demand of nurses in each shift in planning horizon is calculated regarding the priority (vitality) of patients. Results: The stochastic optimization is adapted for our problem and the Sample Average Approximation (SAA) method is used to obtain a optimal schedule with respect to minimizing the regular and over time assignment costs. The results have been analyzed and show the validity of our model. Conclusions: In this model the demand and stay period of patients are stochastic whose the distribution is determined from historical data. We also consider the priority (vitality) of patients in our model.

Enhanced healthcare personnel rostering solution using mobile technologies

We propose a smartphone based software solution for shift scheduling at hospitals.XML Web Services are used to maximize compatibility with Hospital Information Systems and Web Access.The smartphone...

Exploring Nurse Staffing Through Excellence: A Data-Driven Model

Nurse staffing and scheduling are leading issues confronting today's nurse executives as they seek to create high-performance healthcare environments. These environments are regulated, controlled, and governed by accrediting, proprietary agencies, as well as federal, state, and local governments. Nurse leaders are well positioned to meet the intensity, complexity, and emerging requirements of accrediting and regulatory bodies. They have the requisite knowledge and strategic insight to create integrated models of staffing that cross the care continuum towards excellence. As the driver of excellence, nurse leaders recognize the value of integrated staffing models and the need to build the metrics of safety, quality, efficiency, and patient and nurse engagement. It is this notion of excellence and metrics that will add value to healthcare by improving health and impacting financial outcomes among individuals, organizations, and providers. These are essential to achieve the triple aim: better care for individuals, better health for populations, and outcomes at lower per capita costs.

A Constraint Programming and Hybrid Approach to Nurse Rostering Problems

A Constraint Programming and Hybrid Approach to Nurse Rostering Problems

The nurse scheduling problem in real-life.

The aim of this paper is to study the scheduling process for two types of nursing teams, regular teams from care units and the float team that covers for shortages in the hospital. When managers address this problem, they either use a manual approach or have to invest in expensive commercial tool. We propose a simple heuristic approach, flexible and easy enough to be implemented on spreadsheets, and requiring almost no investment. The approach leads to streamlined process and higher-quality schedules for nurses. The multi-objective model and heuristics are presented, and additional analysis is performed to compare the performance of the approach. We show that our approach compares very well with an optimization software (CPLEX solver) and may be implemented at no cost. It addresses the lack of choice between either manual solution method or a commercial package at a high cost.

CONSTRAINT SOVLING ENGINE BASED NURSE ROSTERING WITH INTELLIGENT BACKTRACKING

Efficient utilization of time and effort is essenti al in Personnel scheduling problems to evenly balan ce the workload among the people and attempt to satisfy th e personnel preferences. In Constraint Satisfaction Problem based scheduling problems, when a branch of the search fails the backtracking search algorithm back up to the preceding variable and try a differe nt value for it. So here the most recent decision p oint is revisited. Its run-time complexity for most nontriv ial problems is still exponential. A solution is in telligent backtracking scheme in which backtracking is done directly to the variable that caused the failure. Th is study proposes Constraint Satisfaction Problem base d Nurse Rostering using Intelligent Backtracking approach. The proposed Minimal Critical Set based I ntelligent Backtracking (MCS-IBT) algorithm incorporates Critical Set detection which is follow ed by Minimal Critical Set reduction in order to re duce the search space for nurse rostering. MCS_IBT overcomes missing good MCSs by visiting optimal number of sets. This study finds its applications in sched uling, temporal reasoning, graph problems, floor pl an design, the planning of genetic experiments and the satisfiability problems. The implemented system is tested on the real life data from the hospital and the results shown remarkable performance.

A particle swarm optimization approach with refinement procedure for nurse rostering problem

This study attempts to develop a model satisfying the rules of a typical hospital environment based both on published research data and on requirements of a local hospital under study. A mathematical formulation for the studied nurse rostering problem (NRP) is presented first. Due to the combinatorial nature of the NRP model, a particle swarm optimization (PSO) approach is proposed to solve this highly complicated NRP. The structure of the problem constraints is analyzed and used as base for generating workstretch patterns. These patterns serve as the base for generating fast initial solutions, and will later be improved upon by the proposed PSO algorithm. This study also proposes a simple yet effective procedure for attempting possible refinements on the solutions obtained by the PSO before reporting the final solutions. When fair shift assignment is considered as the decision objective, computational results show that the proposed PSO algorithm with refinement procedure is able to produce optimal solutions in all real test problems in a very efficient manner.

Designing a Data-Driven Decision Support Tool for Nurse Scheduling in the Emergency Department: A Case Study of a Southern New Jersey Emergency Department

A health system serving Burlington and Camden Counties, New Jersey, sought to improve labor productivity for its emergency departments, with emphasis on optimizing nursing staff schedules. Using historical emergency department visit data and operating constraints, a decision support tool was designed to recommend the number of emergency nurses needed in each hour for each day of the week. The pilot emergency department nurse managers used the decision support tool's recommendations to redeploy nurse hours from weekends into a float pool to support periods of demand spikes on weekdays. Productivity improved significantly, with no unfavorable impact on patient throughput, and patient and staff satisfaction. Today's emergency department manager can leverage the increasing ease of access to the emergency department information system's data repository to successfully design a simple but effective tool to support the alignment of its nursing schedule with demand patterns.

Ratios and nurse staffing: the vexed case of emergency departments.

Within Australia nursing unions are pursuing mandated nurse-patient ratios to safeguard patient outcomes and protect their members in healthcare systems where demand perpetually exceeds supply. Establishing ratios for an emergency department is more contentious than for hospital wards. The study's aim was to estimate average staffing levels, skill mix and patient presentations in all New South Wales (NSW) Emergency Departments (EDs). The design was a retrospective historical census audit. Nurse rosters and patient presentation data were collected for three randomly selected census days in May 2010. Twenty-six valid responses out of 44 were returned. A ratio of the number of beds per nurse was calculated as well as skill mix and bed occupancy. The average beds per nurse ratios found were 3.8 (morning shift), 3.6 (evening), and 5.1 (night). However, ratios as high as 8.4 (morning), 7.3 (evening) and 16.0 (night) were identified on particular shifts. Overall a rich skill mix was found with an average of 90% of nursing hours being provided by Registered Nurses. The average daily bed occupancy of 4 patients per bed was similar across ED levels. The study adds to the limited literature on ED staffing and demonstrates the utility in the simplicity of ratios in flagging potential staffing problems. The audit revealed wide variation in staffing levels which was not always linked to patient activity. Of particular concern were the regional EDs (Level 5) which have the capacity to deal with all types of emergencies but where ratios as high as 7 beds per nurse were found during the day. Ratios cannot be used to determine the optimal staffing levels in every clinical situation; their purpose is to force an increase in nursing supply and to prevent individual units from becoming understaffed.

Integrated scheduling of elective surgeries and surgical nurses for operating room suites

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

Solving multi-activity multi-day shift scheduling problems with a hybrid heuristic

This paper addresses a multi-activity multi-day shift scheduling problem in a continuous and flexible environment including a heterogeneous workforce. Given days-off schedules associated with each employee, our objective is to construct and assign admissible multi-activity shifts to employees on their work days in a way that minimizes under-staffing and over-staffing with a restricted budget on workforce cost. A hybrid heuristic which combines tabu search with a branch-and-bound procedure is proposed to solve the problem. The computational experiments prove that our method provides good schedules in relatively short computing times.