TY - GEN
T1 - Out-of-Order Architecture for Real-Time Data-Driven Resilient Planning and Scheduling of Cyber-Physical Manufacturing Systems
AU - Li, Mingxing
AU - Qu, Ting
AU - Yan, Mian
AU - Li, Ming
AU - He, Zhen
AU - Huang, George Q.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/8
Y1 - 2024/8
N2 - The intrinsic stochasticity of manufacturing is one of the main factors that hinder system resilience. Planning and scheduling problems are typical examples plagued by various manufacturing uncertainties such as stochastic processing time, arrivals of new orders, and uncertain breakdowns of stations. Frequent uncertainties regarding operations and resources disturb the workflow, and their cascading effects create chaos in the whole system. The transparency and traceability in Cyber-physical manufacturing systems (CPMS) bring new hope to tackle uncertainties. Inspired by the core spirit of Out-of-Order (OoO) Execution in CPU, this paper proposes a novel OoO architecture, under which production and logistics operations are performed according to real-time executability, for resilient planning and scheduling of CPMS. Computational results have confirmed the effectiveness and resilience of the proposed solution under various uncertainties.
AB - The intrinsic stochasticity of manufacturing is one of the main factors that hinder system resilience. Planning and scheduling problems are typical examples plagued by various manufacturing uncertainties such as stochastic processing time, arrivals of new orders, and uncertain breakdowns of stations. Frequent uncertainties regarding operations and resources disturb the workflow, and their cascading effects create chaos in the whole system. The transparency and traceability in Cyber-physical manufacturing systems (CPMS) bring new hope to tackle uncertainties. Inspired by the core spirit of Out-of-Order (OoO) Execution in CPU, this paper proposes a novel OoO architecture, under which production and logistics operations are performed according to real-time executability, for resilient planning and scheduling of CPMS. Computational results have confirmed the effectiveness and resilience of the proposed solution under various uncertainties.
UR - https://www.scopus.com/pages/publications/85208280251
U2 - 10.1109/CASE59546.2024.10711724
DO - 10.1109/CASE59546.2024.10711724
M3 - Conference article published in proceeding or book
AN - SCOPUS:85208280251
SN - 9798350358520
T3 - IEEE International Conference on Automation Science and Engineering
SP - 3328
EP - 3333
BT - 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PB - IEEE Computer Society
T2 - 20th IEEE International Conference on Automation Science and Engineering, CASE 2024
Y2 - 28 August 2024 through 1 September 2024
ER -