TY - GEN
T1 - Ananlysis and Scheduling for Distributed Robust Production Lines with Bernoulli Machines and Sequence-dependent Setup Time
AU - Zuo, Guanzhong
AU - Jia, Zhiyang
AU - Shi, Jiawei
AU - Wang, Gang
AU - Qi, Yongsheng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Driven by the increasing need of enterprises to meet the demands of multi-cooperation production, the distributed flow shop scheduling problem (DFSP) has emerged as a cutting-edge research topic in the field of flexible manufacturing. Because of the uncertainties in practical production environment of the distributed production lines, the processing efficiency of machines can be undetermined in different scenarios. In this paper, a robust model for DFSP with Bernoulli machines and sequence-dependent setup time is studied. Firstly, the robust model and the analysis of distributed production lines are formulated. Secondly, an improved genetic algorithm with linear rank selection and acceptance criterion is proposed, which has been empirically proven to effectively overcome the issue of being stuck in local optima. Finally, the result of the comparative experiments shows that the proposed improved genetic algorithm can solve the distributed flow shop scheduling problem with uncertain processing efficiency (UPE) and sequence-dependent setup time.
AB - Driven by the increasing need of enterprises to meet the demands of multi-cooperation production, the distributed flow shop scheduling problem (DFSP) has emerged as a cutting-edge research topic in the field of flexible manufacturing. Because of the uncertainties in practical production environment of the distributed production lines, the processing efficiency of machines can be undetermined in different scenarios. In this paper, a robust model for DFSP with Bernoulli machines and sequence-dependent setup time is studied. Firstly, the robust model and the analysis of distributed production lines are formulated. Secondly, an improved genetic algorithm with linear rank selection and acceptance criterion is proposed, which has been empirically proven to effectively overcome the issue of being stuck in local optima. Finally, the result of the comparative experiments shows that the proposed improved genetic algorithm can solve the distributed flow shop scheduling problem with uncertain processing efficiency (UPE) and sequence-dependent setup time.
KW - Bernoulli machine
KW - distributed flow shop scheduling (DFSP)
KW - distributed robust production line
KW - genetic algorithm (GA)
KW - uncertain processing efficiency (UPE)
UR - http://www.scopus.com/inward/record.url?scp=85189344372&partnerID=8YFLogxK
U2 - 10.1109/CAC59555.2023.10451282
DO - 10.1109/CAC59555.2023.10451282
M3 - Conference contribution
AN - SCOPUS:85189344372
T3 - Proceedings - 2023 China Automation Congress, CAC 2023
SP - 1325
EP - 1330
BT - Proceedings - 2023 China Automation Congress, CAC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 China Automation Congress, CAC 2023
Y2 - 17 November 2023 through 19 November 2023
ER -