Nurse workforce scheduling: A qualitative study of Indonesian nurse managers’ experiences during the COVID-19 pandemic

Background: The increase in COVID-19 cases in Indonesia has resulted in changes in the hospital workflow, including the staffing process and scheduling, especially in the isolation units. Nurse managers are working hard in the scheduling system to ensure high-quality care is provided with the best human resources. Objective: This study aimed to explore the experiences of nurse managers in managing staff nurses’ work schedules during the COVID-19 pandemic. Methods: A qualitative descriptive design was used in this study. Eleven nurse managers from three COVID-19 referral hospitals were selected using purposive sampling. Data were collected using online semi-structured interviews. Thematic analysis was used for data analysis, and data were presented using a thematic tree. Consolidated criteria for reporting qualitative research (COREQ) checklist was used as a reporting guideline of the study. Results: Four themes were developed: (i) Nurse shortage, (ii) Strategically looking for ways to fulfill the workforce, (iii) Change of shift schedule, and (iv) Expecting guidance from superiors and compliance from staff. Conclusion: The lack of nurse staff is a problem during a pandemic. Thus, managing personnel effectively, mobilizing and rotating, and recruiting volunteers are strategies to fulfill the workforce during the pandemic. Using a sedentary shift pattern and sufficient holidays could prevent nurses from falling ill and increase compliance with scheduling. In addition, a staffing calculation formula is needed, and top nursing managers are suggested to provide guidance or direction to the head nurses to reduce confusion in managing the work schedule during the pandemic.

Workforce Scheduling with Order-Picking Assignments in Distribution Facilities

Scheduling the availability of order pickers is crucial for effective operations in a distribution facility with manual order pickers. When order-picking activities can only be performed in specific time windows, it is essential to jointly solve the order picker shift scheduling problem and the order picker planning problem of assigning and sequencing individual orders to order pickers. This requires decisions regarding the number of order pickers to schedule, shift start and end times, break times, as well as the assignment and timing of order-picking activities. We call this the order picker scheduling problem and present two formulations. A branch-and-price algorithm and a metaheuristic are developed to solve the problem. Numerical experiments illustrate that the metaheuristic finds near-optimal solutions at 80% shorter computation times. A case study at the largest supermarket chain in The Netherlands shows the applicability of the solution approach in a real-life business application. In particular, different shift structures are analyzed, and it is concluded that the retailer can increase the minimum compensated duration for employed workers from six hours to seven or eight hours while reducing the average labor cost with up to 5% savings when a 15-minute flexibility is implemented in the scheduling of break times.

A Successful Three-Phase Metaheuristic for the Shift Minimization Personal Task Scheduling Problem

Workforce scheduling process consists of three major phases: workload prediction, shift generation, and staff rostering. Shift generation is the process of transforming the determined workload into shifts as accurately as possible. The Shift Minimization Personnel Task Scheduling Problem (SMPTSP) is a problem in which a set of tasks with fixed start and finish times must be allocated to a heterogeneous workforce. We show that the presented three-phase metaheuristic can successfully solve the most challenging SMPTSP benchmark instances. The metaheuristic was able to solve 44 of the 47 instances to optimality. The metaheuristic produced the best overall results compared to the previously published methods. The results were generated as a by-product when solving a more complicated General Task-based Shift Generation Problem. The metaheuristic generated comparable results to the methods using commercial MILP solvers as part of the solution process. The presented method is suitable for application in large real-world scenarios. Application areas include cleaning, home care, guarding, manufacturing, and delivery of goods.