Analysis of priority rule-based scheduling in dual-resource-constrained shop-floor scenarios
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Machine Learning and Systems Engineering. Hrsg. / Sio-long Ao; Burghard Rieger; Mahyar A. Amouzegar. Band 68 LNEE Springer Netherlands, 2010. S. 269-281 (Lecture Notes in Electrical Engineering; Band 68 LNEE).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Analysis of priority rule-based scheduling in dual-resource-constrained shop-floor scenarios
AU - Scholz-Reiter, Bernd
AU - Heger, Jens
AU - Hildebrandt, Torsten
PY - 2010
Y1 - 2010
N2 - A lot of research on scheduling manufacturing systems with priority rules has been done. Most studies, however, concentrate on simplified scenarios considering only one type of resource, usually machines. In this study priority rules are applied to a more realistic scenario, in which machines and operators are dual-constrained and have a re-entrant process flow. Interdependencies of priority rules are analyzed by long-term simulation. Strength and weaknesses of various priority rule combinations are determined at different utilization levels. Further insights are gained by additionally solving static instances optimally by using a mixed integer linear program (MILP) of the production system and comparing the results with those of the priority rules.
AB - A lot of research on scheduling manufacturing systems with priority rules has been done. Most studies, however, concentrate on simplified scenarios considering only one type of resource, usually machines. In this study priority rules are applied to a more realistic scenario, in which machines and operators are dual-constrained and have a re-entrant process flow. Interdependencies of priority rules are analyzed by long-term simulation. Strength and weaknesses of various priority rule combinations are determined at different utilization levels. Further insights are gained by additionally solving static instances optimally by using a mixed integer linear program (MILP) of the production system and comparing the results with those of the priority rules.
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=78651545077&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/636461e9-4252-350a-92da-017c9439b99c/
U2 - 10.1007/978-90-481-9419-3_21
DO - 10.1007/978-90-481-9419-3_21
M3 - Article in conference proceedings
AN - SCOPUS:78651545077
SN - 978-90-481-9418-6
VL - 68 LNEE
T3 - Lecture Notes in Electrical Engineering
SP - 269
EP - 281
BT - Machine Learning and Systems Engineering
A2 - Ao, Sio-long
A2 - Rieger, Burghard
A2 - Amouzegar, Mahyar A.
PB - Springer Netherlands
T2 - International Conference on Advances in Machine Learning and Systems Engineering - 2009
Y2 - 20 October 2009 through 22 October 2009
ER -