Multiobjective optimization for nurse scheduling

Authors:
Peng-Yeng Yin;Chih-Chiang Chao;Ya-Tzu Chiang
Affiliations:
Department of Information Management, National Chi-Nan University, Nantou, Taiwan;Department of Information Management, National Chi-Nan University, Nantou, Taiwan;Department of Information Management, National Chi-Nan University, Nantou, Taiwan
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

Citing 6
Cited 0

Scatter Search: Methodology and Implementations in C

Scatter Search: Methodology and Implementations in C
A 0-1 goal programming model for nurse scheduling

Computers and Operations Research
Multi-objective particle swarm optimization on computer grids

Proceedings of the 9th annual conference on Genetic and evolutionary computation
About selecting the personal best in multi-objective particle swarm optimization

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is laborious to determine nurse scheduling using human-involved manner in order to account for administrative operations, business benefits, and nurse requests. To solve this problem, a mathematical formulation is proposed where the hospital administrators can set multiple objectives and stipulate a set of scheduling constraints. We then present a multiobjective optimization method based on the cyber swarm algorithm (CSA) to solve the nurse scheduling problem. The proposed method incorporates salient features from particle swarm optimization, adaptive memory programming, and scatter search to create benefit from synergy. Two simulation problems are used to evaluate the performance of the proposed method. The experimental results manifest that the proposed method outperforms NSGA II and MOPSO in terms of convergence and diversity performance measures of the produced results.