Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Neuro-Dynamic Programming
Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
Learning to Predict by the Methods of Temporal Differences
Machine Learning
An Object-Oriented Random-Number Package with Many Long Streams and Substreams
Operations Research
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
A multiperiod set covering location model for dynamic redeployment of ambulances
Computers and Operations Research
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Computational methods for static allocation and real-time redeployment of ambulances
Computational methods for static allocation and real-time redeployment of ambulances
Approximate Dynamic Programming for Ambulance Redeployment
INFORMS Journal on Computing
Identifying effective policies in approximate dynamic programming: beyond regression
Proceedings of the Winter Simulation Conference
Emergency medical systems analysis by simulation and optimization
Proceedings of the Winter Simulation Conference
Survey: A review on simulation models applied to emergency medical service operations
Computers and Industrial Engineering
| Hi-index | 0.00 |
Emergency medical service (EMS) providers are charged with the task of managing ambulances so that the time required to respond to emergency calls is minimized. One approach that may assist in reducing response times is ambulance redeployment, i.e., repositioning idle ambulances in real time. We formulate a simulation model of EMS operations to evaluate the performance of a given allocation policy and use this model in an approximate dynamic programming (ADP) context to compute high-quality redeployment policies. We find that the resulting ADP policies perform much better than sub-optimal static policies and marginally better than near-optimal static policies. Representative computational results for Edmonton, Alberta are included.