Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Advances in Intelligent Informatics
Maizatul Farhana Mohamad Nazri
Universiti Teknikal Malaysia Melaka (UTeM)
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
A comparative analysis of classical and cooperative coevolutionary genetic algorithms for solving nurse scheduling problems Maizatul Farhana Mohamad Nazri; Zeratul Izzah Mohd Yusoh; Halizah Basiron; Azlina Daud
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2409

Abstract

The Nurse Scheduling Problem (NSP) is a complex workforce planning task that involves assigning nurses to shifts while satisfying operational feasibility, legal regulations and preference-based quality requirements. Classical Genetic Algorithms (GA) are widely applied to NSP but rely on monolithic optimisation structures that evaluate all constraints within a single population, which can lead to constraint interference and reduced stability as problem realism increases. This study investigates a Cooperative Co-Evolutionary approach for NSP (Coop-NSP), which decomposes optimisation into two interacting subpopulations corresponding to hard and soft constraints. Both subpopulations employ an identical nurse-by-day chromosome representation and evolve independently under specialised objectives, with cooperation introduced through contextual fitness evaluation. Experiments were conducted on a weekly NSP with a seven-day planning horizon and multiple nurse roles, evaluated for 15,000 generations across 30 independent runs under identical parameter settings. The Classical GA achieved a mean best penalty of 651.30 ± 37.90, with a minimum best penalty of 581.7, while Coop-NSP obtained a higher mean best penalty of 768.72 but achieved a lower minimum best penalty of 507.64. The Classical GA exhibited rapid convergence, whereas Coop-NSP demonstrated stepwise convergence with sustained population diversity. Although Coop-NSP incurred higher computational cost, its structured cooperative optimisation framework provides a stable and extensible foundation for future integrated healthcare scheduling research.
Co-Authors Azlina Daud Halizah Basiron Zeratul Izzah Mohd Yusoh