Adaptive neighborhood search for nurse rostering

This paper presents an adaptive neighborhood search method (ANS) for solving the nurse rostering problem proposed for the First International Nurse Rostering Competition (INRC-2010). ANS uses jointly two distinct neighborhood moves and adaptively switches among three intensification and diversification search strategies according to the search history. Computational results assessed on the three sets of 60 competition instances show that ANS improves the best known results for 12 instances while matching the best bounds for 39 other instances. An analysis of some key elements of ANS sheds light on the understanding of the behavior of the proposed algorithm.

[1]  Uwe Aickelin,et al.  Exploiting Problem Structure in a Genetic Algorithm Approach to a Nurse Rostering Problem , 2000, ArXiv.

[2]  Graham Kendall,et al.  A Hybrid Evolutionary Approach to the Nurse Rostering Problem , 2010, IEEE Transactions on Evolutionary Computation.

[3]  Holger H. Hoos,et al.  An adaptive noise mechanism for walkSAT , 2002, AAAI/IAAI.

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

[5]  Roberto Tadei,et al.  A greedy-based neighborhood search approach to a nurse rostering problem , 2004, Eur. J. Oper. Res..

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

[7]  Edmund K. Burke,et al.  A scatter search methodology for the nurse rostering problem , 2010, J. Oper. Res. Soc..

[8]  Ben Paechter,et al.  Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition , 2010, INFORMS J. Comput..

[9]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[10]  Peter I. Cowling,et al.  A Memetic Approach to the Nurse Rostering Problem , 2001, Applied Intelligence.

[11]  Patrick De Causmaecker,et al.  First International Nurse Rostering Competition 2010 (August 10 -13, 2010, Belfast, UK) , 2010 .

[12]  Andreas T. Ernst,et al.  Staff scheduling and rostering: A review of applications, methods and models , 2004, Eur. J. Oper. Res..

[13]  Peter Brucker,et al.  Personnel scheduling: Models and complexity , 2011, Eur. J. Oper. Res..

[14]  Celia A. Glass,et al.  The nurse rostering problem: A critical appraisal of the problem structure , 2010, Eur. J. Oper. Res..

[15]  Jin-Kao Hao,et al.  Adaptive Tabu Search for course timetabling , 2010, Eur. J. Oper. Res..

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

[17]  Edmund K. Burke,et al.  A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems , 2010, Eur. J. Oper. Res..

[18]  C. Mueller,et al.  Nurses' Job Satisfaction: A Proposed Measure , 1990, Nursing research.

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

[20]  Jonathan F. Bard,et al.  Preference scheduling for nurses using column generation , 2005, Eur. J. Oper. Res..

[21]  Uwe Aickelin,et al.  An estimation of distribution algorithm for nurse scheduling , 2007, Ann. Oper. Res..

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

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

[24]  Larry W. Jacobs,et al.  A simulated annealing approach to the cyclic staff-scheduling problem , 1993 .

[25]  Nashat Mansour,et al.  A distributed genetic algorithm for deterministic and stochastic labor scheduling problems , 1999, Eur. J. Oper. Res..

[26]  T. Wauters,et al.  A hyper-heuristic combined with a greedy shuffle approach to the nurse rostering competition , 2010 .

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

[28]  Nicholas Beaumont,et al.  Scheduling staff using mixed integer programming , 1997 .

[29]  Efthymios Housos,et al.  Hybrid optimization techniques for the workshift and rest assignment of nursing personnel , 2000, Artif. Intell. Medicine.

[30]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.