'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

Representativeness, legitimacy and power in public involvement in health-care management

Public participation in health-service management is an increasingly prominent policy internationally. Frequently, though, academic studies have found it marginalized by health professionals who, keen to retain control over decision-making, undermine the legitimacy of involved members of the public, in particular by questioning their representativeness. This paper examines this negotiation of representative legitimacy between staff and involved users by drawing on a qualitative study of service-user involvement in pilot cancer-genetics services recently introduced in England, using interviews, participant observation and documentary analysis. In contrast to the findings of much of the literature, health professionals identified some degree of representative legitimacy in the contributions made by users. However, the ways in which staff and users constructed representativeness diverged significantly. Where staff valued the identities of users as biomedical and lay subjects, users themselves described the legitimacy of their contribution in more expansive terms of knowledge and citizenship. My analysis seeks to show how disputes over representativeness relate not just to a struggle for power according to contrasting group interests, but also to a substantive divergence in understanding of the nature of representativeness in the context of state-orchestrated efforts to increase public participation. This divergence might suggest problems with the enactment of such aspirations in practice; alternatively, however, contestation of representative legitimacy might be understood as reflecting ambiguities in policy-level objectives for participation, which secure implementation by accommodating the divergent constructions of those charged with putting initiatives into practice.