Solving nurse rostering problem using artificial bee colony algorithm

Artificial bee colony algorithm(ABC) is proposed as a new nature-inspired algorithm which has been successfully utilized to tackle numerous class of optimization problems belongs to the category of swarm intelligence optimization algorithms. The major focus of this paper is to show that ABC could be used to generate good solutions when adapted to tackle the nurse rostering problem (NRP). In the proposed ABC for the NRP, the solution methods is divided into two phases. The first uses a heuristic ordering strategy to generate feasible solutions while the second phase employs the usage of ABC algorithm in which its operators are utilized to enhance the feasible solutions to their optimality. The proposed algorithm is tested on a set of 69 problem instances of the dataset introduced by the First International Nurse Rostering Competition 2010 (INRC2010). The results produced by the proposed algorithm are very promising when compared with some existing techniques that worked on the same dataset. Further investigation is still necessary for further improvement of the proposed algorithm.

[1]  Fang He,et al.  A Hybrid Constraint Programming Approach for Nurse Rostering Problems , 2008, SGAI Conf..

[2]  Dušan Teodorović,et al.  Bee Colony Optimization – a Cooperative Learning Approach to Complex Transportation Problems , 2005 .

[3]  Sanja Petrovic,et al.  Enhancing case-based reasoning for personnel rostering with selected tabu search concepts , 2007, J. Oper. Res. Soc..

[4]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[5]  Walter J. Gutjahr,et al.  An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria , 2007, Comput. Oper. Res..

[6]  George Goulas,et al.  A systematic two phase approach for the nurse rostering problem , 2012, Eur. J. Oper. Res..

[7]  Edmund K. Burke,et al.  A Hybrid Tabu Search Algorithm for the Nurse Rostering Problem , 1998, SEAL.

[8]  Mohammed Azmi Al-Betar,et al.  Nurse Rostering Using Modified Harmony Search Algorithm , 2011, SEMCCO.

[9]  Koji Nonobe INRC2010: An Approach Using a General Constraint Optimization Solver , 2010 .

[10]  Mario Vanhoucke,et al.  Branching strategies in a branch-and-price approach for a multiple objective nurse scheduling problem , 2010, J. Sched..

[11]  Mohammed Azmi Al-Betar,et al.  The effect of neighborhood structures on examination timetabling with artificial bee colony , 2012 .

[12]  Mohammed Azmi Al-Betar,et al.  Hybrid Harmony Search for Nurse Rostering Problems , 2013, 2013 IEEE Symposium on Computational Intelligence in Scheduling (CISched).

[13]  Nottingham Ng,et al.  A Hybrid Heuristic Ordering and Variable Neighbourhood Search for the Nurse Rostering Problem , 2005 .

[14]  John J. Bartholdi,et al.  A Guaranteed-Accuracy Round-off Algorithm for Cyclic Scheduling and Set Covering , 1981, Oper. Res..

[15]  Harvey H. Millar,et al.  Cyclic and non-cyclic scheduling of 12 h shift nurses by network programming , 1998 .

[16]  A. Mason,et al.  A Nested Column Generator for solving Rostering Problems with Integer Programming , 1998 .

[17]  Dario Landa Silva,et al.  A heuristic algorithm based on multi-assignment procedures for nurse scheduling , 2013, Ann. Oper. Res..

[18]  Andrew Lim,et al.  Nurse rostering problems - a bibliographic survey , 2003, Eur. J. Oper. Res..

[19]  Sanja Petrovic,et al.  A hybrid metaheuristic case-based reasoning system for nurse rostering , 2009, J. Sched..

[20]  Kathryn A. Dowsland,et al.  Nurse scheduling with tabu search and strategic oscillation , 1998, Eur. J. Oper. Res..

[21]  Jin-Kao Hao,et al.  Adaptive neighborhood search for nurse rostering , 2012, Eur. J. Oper. Res..

[22]  Uwe Aickelin,et al.  An Indirect Genetic Algorithm for a Nurse Scheduling Problem , 2004, Comput. Oper. Res..

[23]  Margarida Moz,et al.  A genetic algorithm approach to a nurse rerostering problem , 2007, Comput. Oper. Res..

[24]  Patrick De Causmaecker,et al.  A hyperheuristic approach to Belgian nurse rostering problems , 2009 .

[25]  Andrzej Jaszkiewicz,et al.  Advanced OR and AI methods in transportation , 2009, Eur. J. Oper. Res..

[26]  Mohammed Azmi Al-Betar,et al.  University course timetabling using hybridized artificial bee colony with hill climbing optimizer , 2014, J. Comput. Sci..

[27]  E. Burke,et al.  Variable neighborhood search for nurse rostering problems , 2004 .

[28]  Patrick De Causmaecker,et al.  The first international nurse rostering competition 2010 , 2010, Ann. Oper. Res..

[29]  Andrew Lim,et al.  A hybrid AI approach for nurse rostering problem , 2003, SAC '03.

[30]  Hendrik Van Landeghem,et al.  The State of the Art of Nurse Rostering , 2004, J. Sched..

[31]  Anthony Wren,et al.  Scheduling, Timetabling and Rostering - A Special Relationship? , 1995, PATAT.

[32]  Mohammed Azmi Al-Betar,et al.  Nurse Scheduling Using Harmony Search , 2011, 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications.

[33]  Patrick De Causmaecker,et al.  Local search neighbourhoods for dealing with a novel nurse rostering model , 2012, Ann. Oper. Res..

[34]  Mohammed Azmi Al-Betar,et al.  Global best Harmony Search with a new pitch adjustment designed for Nurse Rostering , 2013, J. King Saud Univ. Comput. Inf. Sci..

[35]  Mohammed Azmi Al-Betar,et al.  Harmony Search with Novel Selection Methods in Memory consideration for Nurse Rostering Problem , 2014, Asia Pac. J. Oper. Res..

[36]  S. S. Al Sharif,et al.  A 0-1 goal programming model for nurse scheduling , 2005, Comput. Oper. Res..

[37]  Tai-Hsi Wu,et al.  A particle swarm optimization approach with refinement procedure for nurse rostering problem , 2015, Comput. Oper. Res..

[38]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[39]  Erik Demeulemeester,et al.  Personnel scheduling: A literature review , 2013, Eur. J. Oper. Res..

[40]  Mohammed Azmi Al-Betar,et al.  Harmony Search with Greedy Shuffle for Nurse Rostering , 2012, Int. J. Nat. Comput. Res..