Abstract
With the growth of personalized demands, manufacturers need to dynamically collaborate with external resources, forming social production logistics systems (SPLS). The complexity and dynamic nature of these systems increase management difficulty, rendering traditional static decision-making methods unsuitable. This study proposes a reliable dynamic collaborative control method for SPLS in discrete manufacturing environments. It aims to provide a secure and controllable collaborative platform for multiple participants, enhancing the system's resilience to disturbances in dynamic environments. A blockchain and digital twin-based trusted synchronized decision-making framework is designed, enabling real-time and reliable acquisition of comprehensive information to support efficient decision-making. Simultaneously, a blockchain smart contract tree-based trusted synchronized decision-making mechanism is proposed to address dynamic disturbances. Utilizing a collaborative optimization algorithm, the "production-distribution-warehousing" collaborative decision model is optimally coordinated to achieve efficient resource allocation and process management. Using the home appliance manufacturing industry chain as a case study, results show that the proposed trusted synchronized control method outperforms the non-trusted synchronized control method, resulting in a 35.3 % reduction in total system costs and an enhancement in the collaborative operational efficiency of the production logistics system, and ensures reliable and efficient system operation in a dynamic demand environment. This research provides valuable references for the operational management of future production logistics systems.
| Original language | English |
|---|---|
| Article number | 102965 |
| Number of pages | 21 |
| Journal | Robotics and Computer-Integrated Manufacturing |
| Volume | 94 |
| DOIs | |
| Publication status | Published - Aug 2025 |
Keywords
- Blockchain
- Digital twin
- Intelligent decision
- Production logistics synchronization
- Social manufacturing
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- General Mathematics
- Computer Science Applications
- Industrial and Manufacturing Engineering
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