Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Solving Parallel Machine Scheduling Problems by Column Generation
INFORMS Journal on Computing
Operations Research
Least-squares policy iteration
The Journal of Machine Learning Research
Learning Algorithms for Separable Approximations of Discrete Stochastic Optimization Problems
Mathematics of Operations Research
Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems
INFORMS Journal on Computing
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
The optimizing-simulator: An illustration using the military airlift problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Algorithms for Reinforcement Learning
Algorithms for Reinforcement Learning
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
An algorithm for approximating piecewise linear concave functions from sample gradients
Operations Research Letters
| Hi-index | 0.00 |
There are a number of sources of randomness that arise in military airlift operations. However, the cost of uncertainty can be difficult to estimate, and is easy to overestimate if we use simplistic decision rules. Using data from Canadian military airlift operations, we study the effect of uncertainty in customer demands as well as aircraft failures, on the overall cost. The system is first analyzed using the types of myopic decision rules widely used in the research literature. The performance of the myopic policy is then compared to the results obtained using robust decisions that account for the uncertainty of future events. These are obtained by modeling the problem as a dynamic program, and solving Bellman’s equations using approximate dynamic programming. The experiments show that even approximate solutions to Bellman’s equations produce decisions that reduce the cost of uncertainty.