Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Neuro-Dynamic Programming
Stochastic modeling of airlift operations
Proceedings of the 33nd conference on Winter simulation
Cooperative Parallel Tabu Search for Capacitated Network Design
Journal of Heuristics
Operations Research
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The Dynamic Assignment Problem
Transportation Science
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
The Effect of Robust Decisions on the Cost of Uncertainty in Military Airlift Operations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Scheduling fighter aircraft maintenance with reinforcement learning
Proceedings of the Winter Simulation Conference
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There have been two primary modeling and algorithmic strategies for modeling operational problems in transportation and logistics: simulation, offering tremendous modeling flexibility, and optimization, which offers the intelligence of math programming. Each offers significant theoretical and practical advantages. In this article, we show that you can model complex problems using a range of decision functions, including both rule-based and cost-based logic, and spanning different classes of information. We show how different types of decision functions can be designed using up to four classes of information. The choice of which information classes to use is a modeling choice, and requires making specific choices in the representation of the problem. We illustrate these ideas in the context of modeling military airlift, where simulation and optimization have been viewed as competing methodologies. Our goal is to show that these are simply different flavors of a series of integrated modeling strategies.