TY - JOUR
T1 - Machine learning based adaptive production control for a multi-cell flexible manufacturing system operating in a random environment
AU - Arzi, Yohanan
AU - Herbon, Avl
PY - 2000/1/10
Y1 - 2000/1/10
N2 - An adaptive production control approach is used for controlling a multi-cell FMS with machines subject to failures, operating in a highly changing produce-to-order environment. A probabilistic machine learning procedure is integrated within a two-level Distribution Production Control System (DPCS). This enables the DPCS to adapt itself to large fluctuations in demand as well as to other stochastic factors. An extensive simulation study shows that the proposed adaptive control approach significantly improves the production system performance in terms of a combined measure of throughput and order tardiness. The proposed DPCS can be easily implemented as a real-time DPCS due to its simplicity, modularity and the limited information it requires. The proposed adaptive scheme can be integrated in any parametric production control system.
AB - An adaptive production control approach is used for controlling a multi-cell FMS with machines subject to failures, operating in a highly changing produce-to-order environment. A probabilistic machine learning procedure is integrated within a two-level Distribution Production Control System (DPCS). This enables the DPCS to adapt itself to large fluctuations in demand as well as to other stochastic factors. An extensive simulation study shows that the proposed adaptive control approach significantly improves the production system performance in terms of a combined measure of throughput and order tardiness. The proposed DPCS can be easily implemented as a real-time DPCS due to its simplicity, modularity and the limited information it requires. The proposed adaptive scheme can be integrated in any parametric production control system.
UR - http://www.scopus.com/inward/record.url?scp=0034627934&partnerID=8YFLogxK
U2 - 10.1080/002075400189635
DO - 10.1080/002075400189635
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AN - SCOPUS:0034627934
SN - 0020-7543
VL - 38
SP - 161
EP - 185
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 1
ER -