An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems

The efficient and effective management of nursing personnel is of critical importance in a hospital's environment comprising a vast share of the operational costs. The adopted nurse workforce practices and policies highly affect the nurses' working conditions and the provided quality of care. Policy decisions on the staffing level have an impact on the outcome of the scheduling level and vice versa. Isolated reasoning typically leads to suboptimal decisions often resulting in ineffective outcomes of care. In order to overcome these inefficiencies, we propose in this paper a new integrative nurse staffing and shift scheduling approach. We test and assess the benefits of our approach in a reallife environment. Moreover, we examine the impact of several personnel policies on the staffing decision.     

Reconstructing nurse schedules: Computational insights in the problem size parameters

Managing nursing personnel properly is of critical importance as these resources comprise a vast share of the hospital's operational costs. The organisational support and the managerial policy decisions on the scheduling level are important determinants for the nurses' working conditions and the related quality of care. In this paper, we gain insights and understanding in the consequences and outcomes of various personnel re-rostering characteristics and strategies. We explore the boundaries of the time horizon and the nurse staffing size to consider for the nurse re-rostering problem based on computational experiments in a real-life problem environment.     

On the dynamic use of project performance and schedule risk information during projecttracking

Project scheduling, risk analysis and project tracking are key parameters to a project's success or failure. Research on the relative sensitivity of project activities during the project scheduling phase as well as research on project performance measurement during project progress have been published throughout the academic literature and the popular press. Obviously, the interest in activity sensitivity information and project performance measurement from both the academics and the practitioners lies in the need to focus a project manager's attention on those activities that influence the performance of the project. When management has knowledge about the current project performance and has a certain feeling of the relative sensitivity of the various project activities on the project objective, a better management focus and a more accurate response during project tracking should positively contribute to the overall performance of the project.     

Using activity sensitivity and network topology information to monitor project time performance

The interest in activity sensitivity from both the academics and the practitioners lies in the need to focus a project manager's attention on those activities that influence the performance of the project. When management has a certain feeling of the relative sensitivity of the various parts (activities) on the project objective, a better management's focus and a more accurate response during project tracking should positively contribute to the overall performance of the project.     

Multimode time-cost-robustness trade-off project scheduling problem under uncertainty

The time/cost trade-off problem is a well-known project scheduling problem that has been extensively studied. In recent years, many researchers have begun to focus on project scheduling problems under uncertainty to cope with uncertain factors, such as resource idleness, high inventory, and missing deadlines. To reduce the disturbance from uncertain factors, the aim of robust scheduling is to generate schedules with time buffers or resource buffers, which are capped by project makespan and project cost. This paper addresses a time-cost-robustness trade-off project scheduling problem with multiple activity execution modes under uncertainty. A multiobjective optimization model with three objectives (makespan minimization, cost minimization, and robustness maximization) is constructed and three propositions are proposed. An epsilon-constraint method-based genetic algorithm along with three improvement measures is designed to solve this NP-hard problem and to develop Pareto schedule sets, and a large-scale computational experiment on a randomly generated dataset is performed to validate the effectiveness of the proposed algorithm and the improvement measures. The final sensitivity analysis of three key parameters shows their distinctive influences on the three objectives, according to which several suggestions are given to project managers on the effective measures to improve the three objectives.