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Carnegie Mellon Robotics Institute

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Abstract

We have used reinforcement learning together with Monte Carlo simulation to solve a multiperiod production planning problem in a two-stage hybrid manufacturing process (a combination of build-to-plan with build-to-order) with a capacity constraint. Our model minimizes inventory and penalty costs while considering real-world complexities such as different component types sharing the same manufacturing capacity, multiend-products sharing common components, multiechelon bill-of-material (BOM), random lead times, etc. To efficiently search in the huge solution space, we designed a two-phase learning scheme where "good" capacity usage ratios are first found for different decision epochs, based on which a detailed production schedule is further unproved through learning to minimize costs. We illustrate our approach through an example and conclude discussion of future research directions.

Keywords

Proceedings of the 2003 Winter Simulation Conference

Notes

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Intelligent Coordination and Logistics Laboratory

Number of pages: 7

Text Reference

Heng Cao, H. Xi, and Stephen Smith , "A reinforcement learning approach to production planning in the fabrication/fulfillment manufacturing process," December, 2003, pp. 1417 - 1423.

BibTeX Reference

@inproceedings{Cao_2003_5642,
   author = "Heng Cao and H. Xi and Stephen {Smith }",
   title = "A reinforcement learning approach to production planning in the fabrication/fulfillment manufacturing process",
   booktitle = "",
   pages = "1417 - 1423",
   month = "December",
   year = "2003",
   volume = "2",
}

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