Technical Note: \cal Q-Learning
Machine Learning
Neuro-Dynamic Programming
Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems
INFORMS Journal on Computing
A Price-Directed Approach to Stochastic Inventory/Routing
Operations Research
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Dynamic Bid Prices in Revenue Management
Operations Research
Relaxations of Weakly Coupled Stochastic Dynamic Programs
Operations Research
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Ambulance redeployment: an approximate dynamic programming approach
Winter Simulation Conference
Survey: Facility location dynamics: An overview of classifications and applications
Computers and Industrial Engineering
Optimizing emergency supply for mass events
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Exploring bounds on ambulance deployment policy performance
Proceedings of the Winter Simulation Conference
Evaluating dynamic dispatch strategies for emergency medical services: TIFAR simulation tool
Proceedings of the Winter Simulation Conference
Modeling requirements for an emergency medical service system design evaluator
Proceedings of the Winter Simulation Conference
Identifying effective policies in approximate dynamic programming: beyond regression
Proceedings of the Winter Simulation Conference
A Dispatching Model for Server-to-Customer Systems That Balances Efficiency and Equity
Manufacturing & Service Operations Management
A stochastic optimization model for real-time ambulance redeployment
Computers and Operations Research
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We present an approximate dynamic programming approach for making ambulance redeployment decisions in an emergency medical service system. The primary decision is where we should redeploy idle ambulances so as to maximize the number of calls reached within a delay threshold. We begin by formulating this problem as a dynamic program. To deal with the high-dimensional and uncountable state space in the dynamic program, we construct approximations to the value function that are parameterized by a small number of parameters. We tune the parameters using simulated cost trajectories of the system. Computational experiments demonstrate the performance of the approach on emergency medical service systems in two metropolitan areas. We report practically significant improvements in performance relative to benchmark static policies.