A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1

We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control

Scheduling in healthcare with multiple resources

The need for improving efficiency in healthcare is motivated largely by increasing global costs of healthcare. One possibility for improvement is in the optimization of the many schedules found within healthcare. This dissertation focuses on just that for two scheduling problems found within healthcare: the appointment scheduling problem and the master surgery scheduling problem. We first look at the appointment scheduling problem – the problem of assigning time slots to patients booking an appointment at a clinic – examining the various ways in which the randomness of this problem is accounted for, and generalising the problem so that its solutions may be used in a wider range of settings in practice. We consider the application of phase-type distributions as well as simulation and analytical approaches, and we optimize appointment schedules for settings both with multiple healthcare providers, and where patients may arrive in batches rather than one-by-one as is usual. Hereafter, we look at a practical scheduling issue, reporting upon the optimization – via mixed integer linear programming – and subsequent implementation of a surgery schedule for a medium sized hospital in the Netherlands. This problem requires assigning surgical specialties to operate in a given room at a given time during a four-week long repeating schedule; the number of possible combinations of which grows extraordinarily fast, even for a small number of specialties and rooms. In this dissertation, we present the method by which we handled the size of the problem, and pay particular attention to the matter of expectations management throughout the project