Enabling Healthcare 4.0 for Pandemics

11A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling

Tan Nhat Pham and Son Vu Truong Dao^*

International University, Vietnam National University, Ho Chi Minh City, Vietnam

Abstract

In this work, we address the staff scheduling problem in hospitals. The challenge is to assign nurses into various shifts over a certain planning period to meet a number of constraints such as the contractual working time, maximum working time per day and so on. We proposed a method by grouping nurses into clusters, then each cluster is served by a schedule optimized by a hybrid metaheuristic which is a combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). We obtain the results from the hybrid algorithm and compare them with those from the standard PSO, GWO, and a linear programming formulation using IBM CPLEX Studio, to evaluate the effectiveness of our algorithm.

Keywords: Nurse scheduling problem, grey wolf optimization, particle swarm optimization, metaheuristic, hybrid

11.1 Introduction

The main purpose of all nurse scheduling problems is to create the schedules that both satisfy the fit constraints for nurses and the purposes that the hospital would like to achieve. There are 3 shifts for a nurse to work: morning, evening, night shift, and day off. For circumstances happen in all corners of the world today, the COVID-19 pandemic, almost all hospitals have faced a huge shortage of nurses and doctors, which leads to the fact that they have to stay at ...