Location of Repository

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules

Publisher: Palgrave

Year: 2007

OAI identifier:
oai:eprints.nottingham.ac.uk:655

Provided by:
Nottingham ePrints

Downloaded from
http://eprints.nottingham.ac.uk/655/1/07jors_eda.pdf

- (2007). A cyclic preference scheduling of nurses using a Lagrangianbased heuristic.
- (1999). A hybrid tabu search algorithm for the nurse rostering problem.
- (2001). A memetic approach to the nurse rostering problem.
- (2004). An indirect genetic algorithm for a nurse scheduling problem.
- (2002). An indirect genetic algorithm for set covering problems.
- (2004). Building better nurse scheduling algorithms.
- (1995). Cost analysis of alternative formulations for personnel scheduling in continuously operating organisations.
- (2000). Expanding from discrete to continuous estimation of distribution algorithms: The IDEA. In:
- (2000). Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem.
- (2006). Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering.
- (2004). The state of the art of nurse rostering.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.