Coordinated Hospital Patient Scheduling
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Improving Patient Activity Schedules by Multi-agent Pareto Appointment Exchanging
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
Distributed patient scheduling in hospitals
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Logistic-based patient grouping for multi-disciplinary treatment
Artificial Intelligence in Medicine
Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Systematic Review of the Use of Computer Simulation Modeling of Patient Flow in Surgical Care
Journal of Medical Systems
One hyper-heuristic approach to two timetabling problems in health care
Journal of Heuristics
ABMS optimization for emergency departments
Proceedings of the Winter Simulation Conference
Smart Agent-Based Hospital Search, Appointment, and Medical Diagnosis
International Journal of E-Health and Medical Communications
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Scheduling decisions in hospitals are often taken in a decentralized way. This means that different specialized hospital units decide autonomously on patient admissions or operating room schedules. In this paper we present an agent-based model for the selection of an optimal mix for patient admissions. Admitting the right mix of patients is important in order to optimize the resource usage and patient throughput. Our model is based on an extensive case analysis, involving data analysis and interviews, conducted in a case study at a large hospital in the Netherlands. We focus on the coordination of different surgical patient types with probabilistic treatment processes involving multiple hospital units. We also consider the unplanned arrival of other patients requiring (partly) the same hospital resources. Simulation experiments show the applicability of our agent-based decision support tool. The simulation tool allows for the assessment of resource network usage as a function of different policies for decision making. Furthermore, the tool incorporates a first optimization module for the resource allocation of postoperative care beds.