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RI | Publications | A reinforcement learning approach to production planning in the fabrication/fulfillment manufacturing process
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A reinforcement learning approach to production planning in the fabrication/fulfillment manufacturing process
H. Cao, H. Xi, and S. Smith
Vol. 2, December, 2003, pp. 1417 - 1423.
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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.
| Notes |
Associated center: CIMDS
Associated lab/group: Intelligent Coordination and Logistics Laboratory
Number of pages: 7
| Text Reference |
H. Cao, H. Xi, and S. Smith, "A reinforcement learning approach to production planning in the fabrication/fulfillment manufacturing process," Vol. 2, 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",
month = "December",
year = "2003",
volume = "2",
pages = "1417 - 1423"
}