Stochastic production scheduling to meet demand forecasts

Jeff Schneider, Justin Boyan, and Andrew Moore
Proceedings of the 37th IEEE Conference on Decision and Control, December, 1998, pp. 2722 - 2727.


Download
  • Adobe portable document format (pdf) (587KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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",
}