Minimizing total cost in scheduling outpatient appointments
Management Science
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Coordinating Mutually Exclusive Resources using GPGP
Autonomous Agents and Multi-Agent Systems
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Managing Patient Service in a Diagnostic Medical Facility
Operations Research
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
Continual planning and scheduling for managing patient tests in hospital laboratories
Artificial Intelligence in Medicine
Guest editorial: Artificial intelligence in medicine AIME'07
Artificial Intelligence in Medicine
Optimization of Online Patient Scheduling with Urgencies and Preferences
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Input modeling for hospital simulation models using electronic messages
Winter Simulation Conference
Simulation modeling movable hospital assets managed with RFID sensors
Winter Simulation Conference
A Markov decision process approach to multi-category patient scheduling in a diagnostic facility
Artificial Intelligence in Medicine
One hyper-heuristic approach to two timetabling problems in health care
Journal of Heuristics
| Hi-index | 0.00 |
Objective: Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand, for the most efficient use of resources this allocation must be flexible. Methods: We present an adaptive approach to automatic optimization of resource calendars. In our approach, the allocation of capacity to different patient groups is flexible and adaptive to the current and expected future situation. We additionally present an approach to determine optimal resource openings hours on a larger time frame. Our model and its parameter values are based on extensive case analysis at the Academic Medical Hospital Amsterdam. Results and conclusion: We have implemented a comprehensive computer simulation of the application case. Simulation experiments show that our approach of adaptive capacity allocation improves the performance of scheduling patients groups with different attributes and makes efficient use of resource capacity.