• Corpus ID: 16885468

Optimizing Production Manufacturing Using Reinforcement Learning

@inproceedings{Mahadevan1998OptimizingPM,
  title={Optimizing Production Manufacturing Using Reinforcement Learning},
  author={Sridhar Mahadevan and Georgios Theocharous},
  booktitle={FLAIRS Conference},
  year={1998}
}
Many industrial processes involve making parts with an assembly of machines, where each machine carries out an operation on a part, and the finished product requires a whole series of operations. A well-studied example of such a factory structure is the transfer line, which involves a sequence of machines. Optimizing transfer lines has been a subject of much study in the industrial engineering and operations research fields. A desirable goal of a lean manufacturing system is to maximize demand… 

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References

SHOWING 1-10 OF 24 REFERENCES
Hierarchical Decision Making in Stochastic Manufacturing Systems
Most manufacturing systems are large, complex, and subject to uncertainty. Obtaining exact feedback policies to run these systems is nearly impossible, both theoretically and computationally. It is a
Improving Elevator Performance Using Reinforcement Learning
TLDR
Results in simulation surpass the best of the heuristic elevator control algorithms of which the author is aware and demonstrate the power of RL on a very large scale stochastic dynamic optimization problem of practical utility.
Dynamic Programming and Optimal Control
The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential
Reinforcement Learning with Hierarchies of Machines
TLDR
This work presents provably convergent algorithms for problem-solving and learning with hierarchical machines and demonstrates their effectiveness on a problem with several thousand states.
The NSF Workshop on Reinforcement Learning: Summary and Observations
TLDR
The goals of the meeting were to understand limitations of current RL systems and deene promising directions for further research, clarify the relationships between RL and existing work in engineering elds, such as operations research, and identify potential industrial applications of RL.
Reinforcement Learning: A Survey
TLDR
Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Exploiting Structure in Policy Construction
TLDR
This work presents an algorithm, called structured policy Iteration (SPI), that constructs optimal policies without explicit enumeration of the state space, and retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but exploits the variable and prepositional independencies reflected in a temporal Bayesian network representation of MDPs.
A study of the Toyota production system from an industrial engineering viewpoint
* Mechanism of the production function* Improvement of process* Improvement of operation* Development of non-stock production* Interpretation of the Toyota Production System* Mechanism of TPS*
Markov Decision Processes: Discrete Stochastic Dynamic Programming
  • M. Puterman
  • Computer Science
    Wiley Series in Probability and Statistics
  • 1994
TLDR
Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.
Probabilistic Robot Navigation in Partially Observable Environments
TLDR
First results are reported on first results of a research program that uses par tially observable Markov models to robustly track a robots location in office environments and to direct its goal-oriented actions.
...
...