A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

• Published in: Robotics and computer-integrated manufacturing Vol. 71; p. 102160
• Main Authors: Zhou, Bin, Bao, Jinsong, Li, Jie, Lu, Yuqian, Liu, Tianyuan, Zhang, Qiwan
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2021; Elsevier BV
• Subjects: Algorithms; Community-based device; Customization; Decision making; Discrete manufacturing; Knowledge; Knowledge graph; Knowledge representation; Machine learning; Machining; Manufacturing; Mixed-model production; Ontology; Optimization; Resource allocation; Semantics; Workshops; Mixed-model production; Discrete manufacturing; Ontology; Community-based device; Resource allocation; Knowledge graph
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2021.102160

The main contributions of this paper are as follows:•Proposing a unified knowledge graph-based decision-making framework that integrates the implicit engineering knowledge in machining workshop environment. The framework was used for supporting the optimal method of resource allocation.•Developing a distributed representation learning algorithm to mine implicit relationships between complex engineering data, and enrich the relationship between workshop resources to efficiently guide production.•Presenting a three-staged method of candidate device formation and community-based device evaluation in the mixed-model production process, which uses the WRKG of machining workshop to provide relatedness data support for the formation and evaluation of candidate device.•Evaluating the proposed approach by employing the production task of structural parts in aerospace machining workshop. The results indicate that the method can generate a more logical and intuitive resource reconfiguration process knowledge to improve the device utilization rate, the response capability of processing tasks and the flexibility of devices under the premise of stable processing. Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.