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

Chun-Cheng Lin, Jia Rong Kang, Ding Jung Chiang, Chien Liang Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Article number595419
JournalInternational Journal of Distributed Sensor Networks
Volume2015
DOIs
StatePublished - 1 Jan 2015

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