IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i3d10.1007_s10845-021-01854-4.html
   My bibliography  Save this article

A context-aware recommendation system for improving manufacturing process modeling

Author

Listed:
  • Jiaxing Wang

    (Zhejiang University of Technology)

  • Sibin Gao

    (Research Institute of CETHIK Group)

  • Zhejun Tang

    (Zhejiang University/University of Illinois at Urbana-Champaign (ZJU-UIUC) Institute)

  • Dapeng Tan

    (Zhejiang University of Technology
    Ministry of Education & Zhejiang Province
    Zhejiang Province & Ministry of Education)

  • Bin Cao

    (Zhejiang University of Technology)

  • Jing Fan

    (Zhejiang University of Technology)

Abstract

Process recommendation is an essential technique to help process modeler effectively and efficiently model a manufacturing process from scratch. However, the current process recommendation methods suffer from the following problems: (1) To extract all the execution paths from a manufacturing process, the behavior-based methods may occur a state space explosion problem when unfolding a process with multiple parallel patterns, resulting in low efficiency. (2) Current structure-based methods are inefficient since too many expensive computations of the graph edit distance are involved. (3) Most of the existing methods manually design their process similarity metrics with several features, which can only be applied in specific situations. (4) Few works provide visualization tools for process modeling assistance. To resolve these problems, this paper proposes a context-aware recommendation system for improving manufacturing process modeling. First, the independent paths and P,Q-grams are efficiently extracted from the manufacturing processes in the repository to represent their typical behavior and structure. Then, the process recommendation problem is transformed into the word prediction problem in natural language processing, where the serialization of an independent path/P,Q-gram and a node in it are separately regarded as a sentence and a word. The Word2vec model is introduced to automatically learn the relationships among nodes from independent paths and P,Q-grams and generate the vectors with hundreds of context-aware features for nodes in the repository. After that, the top-k similar nodes are recommended for the target node in the process fragment under construction based on the k-nearest neighbors algorithm. Finally, a visualization tool is provided for process modelers to efficiently design a new manufacturing process. Experimental evaluations show that the proposed method can perform similar or even better than the baseline methods in terms of recommending quality.

Suggested Citation

  • Jiaxing Wang & Sibin Gao & Zhejun Tang & Dapeng Tan & Bin Cao & Jing Fan, 2023. "A context-aware recommendation system for improving manufacturing process modeling," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1347-1368, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01854-4
    DOI: 10.1007/s10845-021-01854-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01854-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01854-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Lin & Tan, Dapeng & Wang, Tong & Yin, Zichao & Fan, Xinghua & Wang, Ronghui, 2021. "Multiphase coupling mechanism of free surface vortex and the vibration-based sensing method," Energy, Elsevier, vol. 216(C).
    2. Victor R. L. Shen & Cheng-Ying Yang & Rong-Kuan Shen & Yu-Chia Chen, 2018. "Application of Petri nets to deadlock avoidance in iPad-like manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1363-1378, August.
    3. D. G. Mogale & Sri Krishna Kumar & Manoj Kumar Tiwari, 2020. "Green food supply chain design considering risk and post-harvest losses: a case study," Annals of Operations Research, Springer, vol. 295(1), pages 257-284, December.
    4. Libin Han & Keyi Xing & Xiao Chen & Fuli Xiong, 2018. "A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1083-1096, June.
    5. Hyun Ahn & Tai-Woo Chang, 2019. "A Similarity-Based Hierarchical Clustering Method for Manufacturing Process Models," Sustainability, MDPI, vol. 11(9), pages 1-18, May.
    6. Dapeng Tan & Libin Zhang & Qinglin Ai, 2019. "An embedded self-adapting network service framework for networked manufacturing system," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 539-556, February.
    7. Li, Lin & Tan, Dapeng & Yin, Zichao & Wang, Tong & Fan, Xinghua & Wang, Ronghui, 2021. "Investigation on the multiphase vortex and its fluid-solid vibration characters for sustainability production," Renewable Energy, Elsevier, vol. 175(C), pages 887-909.
    8. Andrej Tibaut & Danijel Rebolj & Matjaž Nekrep Perc, 2016. "Interoperability requirements for automated manufacturing systems in construction," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 251-262, February.
    9. D. G. Mogale & Naoufel Cheikhrouhou & Manoj Kumar Tiwari, 2020. "Modelling of sustainable food grain supply chain distribution system: a bi-objective approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(18), pages 5521-5544, September.
    10. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wattana Viriyasitavat & Li Xu & Zhuming Bi & Assadaporn Sapsomboon, 2020. "Blockchain-based business process management (BPM) framework for service composition in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1737-1748, October.
    2. Cosmena Mahapatra & Ashish Payal & Meenu Chopra, 2020. "Swarm intelligence based centralized clustering: a novel solution," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1877-1888, December.
    3. Yiying Zhang & Aining Chi, 2023. "Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1547-1571, April.
    4. G. Cherif & E. Leclercq & D. Lefebvre, 2023. "Scheduling of a class of partial routing FMS in uncertain environments with beam search," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 493-514, February.
    5. Li, Lin & Gu, Zeheng & Xu, Weixin & Tan, Yunfeng & Fan, Xinghua & Tan, Dapeng, 2023. "Mixing mass transfer mechanism and dynamic control of gas-liquid-solid multiphase flow based on VOF-DEM coupling," Energy, Elsevier, vol. 272(C).
    6. Xinnian Wang & Keyi Xing & Chao-Bo Yan & Mengchu Zhou, 2019. "A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems," Complexity, Hindawi, vol. 2019, pages 1-14, June.
    7. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Juan Lu & Chengyi Ou & Chen Liao & Zhenkun Zhang & Kai Chen & Xiaoping Liao, 2021. "Formal modelling of a sheet metal smart manufacturing system by using Petri nets and first-order predicate logic," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1043-1063, April.
    9. Yang, Lin & Pang, Shujiang & Wang, Xiaoyan & Du, Yi & Huang, Jieyu & Melching, Charles S., 2021. "Optimal allocation of best management practices based on receiving water capacity constraints," Agricultural Water Management, Elsevier, vol. 258(C).
    10. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    11. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    12. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    13. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    14. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    15. Mohd Fahmi Bin Mad Ali & Mohd Khairol Anuar Bin Mohd Ariffin & Aidin Delgoshaei & Faizal Bin Mustapha & Eris Elianddy Bin Supeni, 2023. "A Comprehensive 3-Phase Framework for Determining the Customer’s Product Usage in a Food Supply Chain," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    16. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    17. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    18. Ming Zhang & Qianwen Huang & Sihan Liu & Huiying Li, 2019. "Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment," Sustainability, MDPI, vol. 11(23), pages 1-27, November.
    19. Silvia Carpitella & Ilyas Mzougui & Joaquín Izquierdo, 2022. "Multi-criteria risk classification to enhance complex supply networks performance," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 769-785, September.
    20. Ruidi Chen & Ioannis Ch. Paschalidis, 2022. "Robust Grouped Variable Selection Using Distributionally Robust Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 1042-1071, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01854-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.