Research on digital workshop dynamic scheduling technology based on blockchain
DOI:
https://doi.org/10.4108/eetsis.13503Keywords:
Blockchain, Dynamic Scheduling, Cloud-Edge Collaboration, Multi-objective Genetic Algorithm, Smart ContractAbstract
INTRODUCTION: As a critical process in high-end equipment manufacturing, dynamic scheduling in digital workshops plays a vital role in order delivery and project cost control. However, traditional scheduling frameworks suffer from weak data security, opaque multi-agent collaboration, and slow constraint checking.
OBJECTIVES: To address these limitations, this study proposes an efficient and trustworthy dynamic scheduling solution that balances operational efficiency with data reliability.
METHODS: A three-layer collaborative framework comprising cloud, edge, and blockchain layers is designed. The cloud layer employs a multi-strategy improved multi-objective genetic algorithm with three-stage encoding targeting processes, equipment, and job teams. This algorithm integrates tournament selection, adaptive repair, and particle swarm optimization to globally minimize maximum delivery time, total energy consumption, and equipment load fluctuation. The blockchain layer leverages consortium blockchain and smart contracts for tamper-proof data storage and automatic constraint verification, while the edge layer handles real-time on-site scheduling.
RESULTS: The optimization algorithm converges to an optimal value of 55.1 in an average of only 3.9 iterations, outperforming three mainstream heuristic algorithms. In a real deployment across 57 workstations in a cruise ship laser sheet metal workshop, scheduling tasks are completed within 25 seconds. Blockchain technology achieves a 100% data tampering detection rate, improves data consistency from 96.2% to 99.8%, and reduces scheduling deviation from 4.39% to 3.82%, with only a 3-second processing overhead.
CONCLUSION: The integrated architecture enhances both scheduling efficiency and data reliability, supporting the digital and intelligent transformation of shipbuilding enterprises.
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Copyright (c) 2026 Ganlong Wang, Jun Zhu, Guoyin Zhang, Jingjing Sun, Wei Zheng, Bin Xie, Xiang Li
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