TY - GEN
T1 - Dynamic Multi-objective Opti-State Decision-Making Method for Intermittent Synchronized Production Operation System
AU - Zhang, Kai
AU - Yi, Honglin
AU - Qu, Ting
AU - Zeng, Meihua
AU - Ma, Lin
AU - Li, Congdong
AU - Huang, George Q.
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024/9
Y1 - 2024/9
N2 - In the era of escalating customer demands for tailored products, advancements in intelligent manufacturing technologies and the proliferation of diverse production and operational models, production-logistics systems face heightened internal and external disruptions. This work contributes a novel dynamic multi-objective opti-state decision-making framework and method to address the complex decision-making challenges that arise from such disruptions. It delves into the re-decision-making requirements for maintaining optimal state performance in production-logistics systems amidst disturbances, focusing on operational objectives. The proposed method employs intelligent algorithms for local sub-models, utilizing the gray target theory to determine the best dynamic multi-objective optimization strategy. To demonstrate its practicality, the method is instantiated as an intermittent synchronized production operation system, with a case study in the context of enterprises with intermittent production, validating the effectiveness of the proposed approach.
AB - In the era of escalating customer demands for tailored products, advancements in intelligent manufacturing technologies and the proliferation of diverse production and operational models, production-logistics systems face heightened internal and external disruptions. This work contributes a novel dynamic multi-objective opti-state decision-making framework and method to address the complex decision-making challenges that arise from such disruptions. It delves into the re-decision-making requirements for maintaining optimal state performance in production-logistics systems amidst disturbances, focusing on operational objectives. The proposed method employs intelligent algorithms for local sub-models, utilizing the gray target theory to determine the best dynamic multi-objective optimization strategy. To demonstrate its practicality, the method is instantiated as an intermittent synchronized production operation system, with a case study in the context of enterprises with intermittent production, validating the effectiveness of the proposed approach.
KW - Optimal State
KW - Synchronized Decision
KW - Uncertainties
UR - https://www.scopus.com/pages/publications/85204635520
U2 - 10.1007/978-3-031-71637-9_31
DO - 10.1007/978-3-031-71637-9_31
M3 - Conference article published in proceeding or book
AN - SCOPUS:85204635520
SN - 9783031716362
T3 - IFIP Advances in Information and Communication Technology
SP - 460
EP - 473
BT - Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments - 43rd IFIP WG 5.7 International Conference, APMS 2024, Proceedings
A2 - Thürer, Matthias
A2 - Riedel, Ralph
A2 - von Cieminski, Gregor
A2 - Romero, David
PB - Springer Science and Business Media Deutschland GmbH
T2 - 43rd IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2024
Y2 - 8 September 2024 through 12 September 2024
ER -