SCHEDULING NURSES USING A TABU-SEARCH HYPERHEURISTIC

Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. A number of hyperheuristics have been developed over the past few years. Here we propose a new hyperheuristic framework within which heuristics compete against one another. The rules for competition are motivated by the principles of reinforcement learning. We analyse the differences between a previously published choice function hyperheuristic and the new hyperheuristic. We demonstrate how the new hyperheuristic can make further improvements when a number of features are incorporated, including a dynamic tabu list which forbids the use of certain heuristics at certain times. The result is an algorithm which is competitive with the choice function hyperheuristic when applied to a comprehensive suite of nurse scheduling problems at a major UK hospital, featuring a wide variety of solution landscapes.

[1]  Eric Soubeiga,et al.  Development and application of hyperheuristics to personnel scheduling , 2003 .

[2]  Graham Kendall,et al.  Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling , 2002, PPSN.

[3]  P. Cowling,et al.  CHOICE FUNCTION AND RANDOM HYPERHEURISTICS , 2002 .

[4]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[5]  Alexander Nareyek,et al.  Choosing search heuristics by non-stationary reinforcement learning , 2004 .

[6]  Graham Kendall,et al.  Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation , 2002, EvoWorkshops.

[7]  S. S. Panwalkar,et al.  A Survey of Scheduling Rules , 1977, Oper. Res..

[8]  M. Gendreau.  Chapter 2 AN INTRODUCTION TO TABU SEARCH , 2005 .

[9]  Spyros Makridakis,et al.  Forecasting Methods for Management , 1989 .

[10]  Egon Balas,et al.  The Shifting Bottleneck Procedure for Job Shop Scheduling , 1988 .

[11]  R. Storer,et al.  New search spaces for sequencing problems with application to job shop scheduling , 1992 .

[12]  Graham Kendall,et al.  An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[13]  Peter Ross,et al.  Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems , 2002, GECCO.

[14]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[15]  S. Griffis.  EDITOR , 1997, Journal of Navigation.

[16]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[17]  Alexander Nareyek,et al.  An Empirical Analysis of Weight-Adaptation Strategies for Neighborhoods of Heuristics , 2001 .

[18]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

[19]  Fred Glover,et al.  PROBABILISTIC AND PARAMETRIC LEARNING COMBINATIONS OF LOCAL JOB SHOP SCHEDULING RULES , 1963 .

[20]  Richard S. Sutton,et al.  Dimensions of Reinforcement Learning , 1998 .

[21]  Graham Kendall,et al.  A Hyperheuristic Approach to Scheduling a Sales Summit , 2000, PATAT.

[22]  Jan Karel Lenstra,et al.  A local search template , 1998, Comput. Oper. Res..

[23]  Peter Ross,et al.  A Heuristic Combination Method for Solving Job-Shop Scheduling Problems , 1998, PPSN.

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

[25]  H. Terashima-Marín,et al.  Evolution of Constraint Satisfaction strategies in examination timetabling , 1999 .

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

[27]  Peter Ross,et al.  A Promising Hybrid GA/Heuristic Approach for Open-Shop Scheduling Problems , 1994, ECAI.

[28]  Gilbert Laporte,et al.  Metaheuristics: A bibliography , 1996, Ann. Oper. Res..

[29]  Kathryn A. Dowsland,et al.  Solving a nurse scheduling problem with knapsacks, networks and tabu search , 2000, J. Oper. Res. Soc..