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Carnegie Mellon Robotics Institute

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Abstract

Production scheduling, the problem of sequentially configuring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. We describe a Markov decision process (MDP) formulation of production scheduling which captures stochasticity, while retaining the ability to construct a schedule to meet demand forecasts. The solution to this MDP is a value function, specific to the current demand forecasts, which can be used to generate optimal scheduling decisions online. We then describe an industrial application and a reinforcement learning method for generating an approximate value function in this domain. Our results demonstrate that in both deterministic and noisy scenarios, value function approximation is an effective technique.

Notes

Text Reference

Jeff Schneider, Justin Boyan, and Andrew Moore, "Stochastic production scheduling to meet demand forecasts," Proceedings of the 37th IEEE Conference on Decision and Control, December, 1998, pp. 2722 - 2727.

BibTeX Reference

@inproceedings{Schneider_1998_3662,
   author = "Jeff Schneider and Justin Boyan and Andrew Moore",
   title = "Stochastic production scheduling to meet demand forecasts",
   booktitle = "Proceedings of the 37th IEEE Conference on Decision and Control",
   pages = "2722 - 2727",
   month = "December",
   year = "1998",
   volume = "3",
}

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