Reinforcement Learning
Neuro-Dynamic Programming
Operations Research
Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems
INFORMS Journal on Computing
Mathematical and Computer Modelling: An International Journal
The optimizing-simulator: merging simulation and optimization using approximate dynamic programming
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Approximate dynamic programming: lessons from the field
Proceedings of the 40th Conference on Winter Simulation
Sourcing strategies in supply risk management: An approximate dynamic programming approach
Computers and Operations Research
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There has long been a competition between simulation and optimization in the modeling of problems in transportation and logistics, machine scheduling and similar high-dimensional problems in operations research. Simulation strives to model operations, often using rule-based logic. Optimization strives to find the best possible solution, minimizing costs or maximizing profits. In this tutorial, we show how these two modeling technologies can be brought together, combining the flexibility of simulation with the intelligence of optimization.