Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering

Personnel rostering problems are highly constrained resource allocation problems. Human rostering experts have many years of experience in making rostering decisions which reflect their individual goals and objectives. We present a novel method for capturing nurse rostering decisions and adapting them to solve new problems using the Case-Based Reasoning (CBR) paradigm. This method stores examples of previously encountered constraint violations and the operations that were used to repair them. The violations are represented as vectors of feature values. We investigate the problem of selecting and weighting features so as to improve the performance of the case-based reasoning approach. A genetic algorithm is developed for off-line feature selection and weighting using the complex data types needed to represent real-world nurse rostering problems. This approach significantly improves the accuracy of the CBR method and reduces the number of features that need to be stored for each problem. The relative importance of different features is also determined, providing an insight into the nature of expert decision making in personnel rostering.

[1]  Slim Abdennadher,et al.  INTERDIP - An Interactive Constraint Based Nurse Scheduler , 1999 .

[2]  David Beasley,et al.  An overview of genetic algorithms: Part 1 , 1993 .

[3]  David B. Beasley,et al.  An overview of genetic algorithms: Part 1 , 1993 .

[4]  James E. Bailey,et al.  Integrated days off and shift personnel scheduling , 1985 .

[5]  Sanja Petrovic,et al.  Storing and Adapting Repair Experiences in Employee Rostering , 2002, PATAT.

[6]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[7]  Lakhmi C. Jain,et al.  Nearest neighbor classifier: Simultaneous editing and feature selection , 1999, Pattern Recognit. Lett..

[8]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

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

[10]  Sanja Petrovic,et al.  A novel approach to finding feasible solutions to personnel rostering problems , 2003 .

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

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Lawrence Davis,et al.  A Hybrid Genetic Algorithm for Classification , 1991, IJCAI.

[14]  Jimmy Ho-Man Lee,et al.  A constraint-based nurse rostering system using a redundant modeling approach , 1996, Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence.

[15]  David W. Aha,et al.  A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.

[16]  E.K. Burke,et al.  Fitness evaluation for nurse scheduling problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[17]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[18]  David W. Aha,et al.  Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[19]  Steve Scott,et al.  Case-Bases Incorporating Scheduling Constraint Dimensions - Experiences in Nurse Rostering , 1998, EWCBR.

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

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

[22]  Harald Meyer auf'm Hofe.  Solving Rostering Tasks as Constraint Optimization , 2000, PATAT.

[23]  Steven Salzberg,et al.  A Nearest Hyperrectangle Learning Method , 1991, Machine Learning.

[24]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[25]  Sanja Petrovic,et al.  Case-based reasoning in employee rostering: learning repair strategies from domain experts , 2002 .

[26]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[27]  D. Michael Warner,et al.  Scheduling Nursing Personnel According to Nursing Preference: A Mathematical Programming Approach , 1976, Oper. Res..

[28]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .