An Effective Resource Matching Scheme Based on a Novel Unified Descriptive Model for Modern Manufacturing Industry Systems

In order to effectively solve the problem of heterogeneous design/manufacturing/service resources and isolation in the whole lifecycle and realize unified description of design/manufacturing/ service resources and resource sharing across subjects and stages, this paper proposes a hierarchical and mo...

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Bibliographic Details
Main Authors: Lin, J. (Author), Wu, C. (Author), Yared, R. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02130nam a2200205Ia 4500
001 0.3390-electronics11081187
008 220421s2022 CNT 000 0 und d
020 |a 20799292 (ISSN) 
245 1 0 |a An Effective Resource Matching Scheme Based on a Novel Unified Descriptive Model for Modern Manufacturing Industry Systems 
260 0 |b MDPI  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3390/electronics11081187 
520 3 |a In order to effectively solve the problem of heterogeneous design/manufacturing/service resources and isolation in the whole lifecycle and realize unified description of design/manufacturing/ service resources and resource sharing across subjects and stages, this paper proposes a hierarchical and modularized ontology-based resource-unified descriptive model, according to the characteristics of design/manufacturing/service resources. We analyze all kinds of properties of the resources, design a specific descriptive model of ontology, function, and service, ensure the consistency and independence of resource descriptions, and use the OWL (Web Ontology Language) ontology descriptive language and Protégé tools to verify. Then, based on the unified descriptive model, a resource matching method based on multi-level tags is proposed, which matches the task request with the resources in the resource library, selects the resources that meet the task request, and guarantees the resource sharing across subjects and stages. The resource matching work first performs task description and decomposition, and uses information entropy and rough set theory to sort the importance of subtasks, then uses the semantic similarity algorithm to complete multi-level tags’ matching. Finally, two examples are used to prove the feasibility and effectiveness of the experiment. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 
650 0 4 |a multi-level tags 
650 0 4 |a ontology modeling 
650 0 4 |a resource matching 
650 0 4 |a the whole lifecycle 
700 1 0 |a Lin, J.  |e author 
700 1 0 |a Wu, C.  |e author 
700 1 0 |a Yared, R.  |e author 
773 |t Electronics (Switzerland)