.png){kind=icon}
.png){kind=icon}
Return
Dynamic resource configuration approach for production-logistics synchronization systems based on predictive opti-state control strategy
.png){kind=icon}
PRE
AI
Y
T
Z
Z
M
G
DOI：10.1080/0951192X.2026.2642262.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Production – logistics synchronization systems face increasing challenges from volatile market demand and stringent resource constraints in the era of personalized manufacturing. Conventional forecasting and static resource configuration methods often fail to deliver responsiveness, cost efficiency, and adaptability under such uncertainty. To address these limitations, this study develops an integrated Predictive Opti-State Control (POsC) framework for dynamic resource configuration. Guided by the POsC strategy, a hybrid Temporal Convolutional Network – Long Short-Term Memory (TCN – LSTM) model is employed to capture nonlinear demand fluctuations and long-term dependencies across multiple product categories. Based on forecasting outputs, a dynamic resource configuration model is formulated to minimize total system costs, including production, transition, storage, and rental costs, under multi-resource constraints. To efficiently solve the resulting combinatorial optimization problem, an Improved Simulated Annealing (ISA) algorithm is designed, which enhances global exploration and local exploitation capabilities. The proposed approach is validated through an industrial case study of a leading paint manufacturer in the Guangdong – Hong Kong – Macao Greater Bay Area, where idle equipment capacity and excessive rental costs frequently arise due to demand uncertainty. Experimental analysis shows that the integrated framework significantly outperforms conventional forecasting and heuristic configuration methods, achieving superior performance in demand prediction accuracy, resource utilization, cost reduction, and system flexibility.
Keywords:
Production-logistics system
resource configuration
predictive opti-state control
big data
Journal
I
IF:
4
Papers:
2.3K
Citations:
3.4K
Organization
Cited Papers