TY - GEN
T1 - Hybrid harmony search for nurse rostering problems
AU - Awadallah, Mohammed A.
AU - Khader, Ahamad Tajudin
AU - Al-Betar, Mohammed Azmi
AU - Bolaji, Asaju La Aro
PY - 2013
Y1 - 2013
N2 - In this paper, a Hybrid Harmony Search Algorithm (HHSA) is presented for Nurse Rostering Problem (NRP) using the dataset proposed by the First International Nurse Rostering Competition (INRC2010). NRP is tackled by assigning daily shifts to nurses with different skills and working contracts, subject to hard and soft constraints. Harmony Search Algorithm (HSA) is a recent evolutionary computing technique, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments. Recently, HSA has been used for NRP, with promising results. This paper extends HSA to HHSA by adding two powerful concepts to HSA: (i) hybridization with hill climbing optimizer to improve the exploitation ability, and (ii) hybridization with global-best concept of particle swarm optimization to improve the speed of convergence. The proposed HHSA is evaluated against a dataset provided by INRC2010. The results show that it is a powerful technique for INRC2010 dataset. A comparative analysis with five competitive methods is conducted. HHSA outperforms the other competitive methods in three instances and obtained the best results in 29 others out of 69 instances. The efficiency of our method lends further support to the previous theory based on hybridizing the local search within evolutionary computing technique for hard combinatorial optimization problems.
AB - In this paper, a Hybrid Harmony Search Algorithm (HHSA) is presented for Nurse Rostering Problem (NRP) using the dataset proposed by the First International Nurse Rostering Competition (INRC2010). NRP is tackled by assigning daily shifts to nurses with different skills and working contracts, subject to hard and soft constraints. Harmony Search Algorithm (HSA) is a recent evolutionary computing technique, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments. Recently, HSA has been used for NRP, with promising results. This paper extends HSA to HHSA by adding two powerful concepts to HSA: (i) hybridization with hill climbing optimizer to improve the exploitation ability, and (ii) hybridization with global-best concept of particle swarm optimization to improve the speed of convergence. The proposed HHSA is evaluated against a dataset provided by INRC2010. The results show that it is a powerful technique for INRC2010 dataset. A comparative analysis with five competitive methods is conducted. HHSA outperforms the other competitive methods in three instances and obtained the best results in 29 others out of 69 instances. The efficiency of our method lends further support to the previous theory based on hybridizing the local search within evolutionary computing technique for hard combinatorial optimization problems.
KW - Evolutionary algorithm
KW - Harmony search
KW - Hill climbing
KW - Metaheuristic
KW - Nurse rostering
UR - https://www.scopus.com/pages/publications/84886702179
U2 - 10.1109/SCIS.2013.6613253
DO - 10.1109/SCIS.2013.6613253
M3 - Conference contribution
AN - SCOPUS:84886702179
SN - 9781467359092
T3 - Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Scheduling, CISched 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 60
EP - 67
BT - Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Scheduling, CISched 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
T2 - 2013 IEEE Symposium on Computational Intelligence in Scheduling, CISched 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -