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

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.

The code document's structure and analysis

The purpose of this paper is twofold. Firstly it presents a preliminary and ethnomethodologically-informed analysis of the way in which the growing structure of a particular program's code was ongoingly derived from its earliest stages. This was motivated by an interest in how the detailed structure of completed program `emerged from nothing' as a product of the concrete practices of the programmer within the framework afforded by the language. The analysis is broken down into three sections that discuss: the beginnings of the program's structure; the incremental development of structure; and finally the code productions that constitute the structure and the importance of the programmer's stock of knowledge. The discussion attempts to understand and describe the emerging structure of code rather than focus on generating `requirements' for supporting the production of that structure. Due to time and space constraints, however, only a relatively cursory examination of these features was possible. Secondly the paper presents some thoughts on the difficulties associated with the analytic---in particular ethnographic---study of code, drawing on general problems as well as issues arising from the difficulties and failings encountered as part of the analysis presented in the first section.

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

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.

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

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.

Health, hygiene and biosecurity: tribal knowledge claims in the UK poultry industry

Since 1997 the world has been facing the threat of a human influenza pandemic that may be caused by an avian virus and the poultry industry around the globe has been grappling with the highly pathogenic strain of avian influenza H5N1, or in more informal terms bird flu. The UK poultry industry has lived with and through this threat and its consequences since 2005. This study investigates knowledge claims about health, hygiene and biosecurity as tools to ward off the threat from this virus. It takes a semi-ethnographic and discourse analytic approach to analyse a small corpus of semi-structured interviews carried out in the wake of one of the most publicised outbreaks of H5N1 in Suffolk in 2007. It reveals that claims about what best to do to protect flocks against the risk of disease are divided along lines imposed on the one hand by the structure of the industry and on the other by more 'tribal' lines drawn by knowledge and belief systems about purity and dirt, health and hygiene.