Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

Computer Science – Neural and Evolutionary Computing

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

10.1002/(SICI)1099-1425(200005/0

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-682514

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.