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A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration

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  • Nagesh Shukla
  • Senevi Kiridena

Abstract

Considering the need for more effective decision support in the context of distributed manufacturing, this paper develops an advanced analytics framework for configuring supply chain (SC) networks. The proposed framework utilises a distributed multi-agent system architecture to deploy fuzzy rough sets-based algorithms for knowledge elicitation and representation. A set of historical sales data, including network node-related information, is used together with the relevant details of product families to predict SC configurations capable of fulfilling desired customer orders. Multiple agents such as data retrieval agent, knowledge acquisition agent, knowledge representation agent, configuration predictor agent, evaluator agent and dispatching agent are used to help execute a broad spectrum of SC configuration decisions. The proposed framework considers multiple product variants and sourcing options at each network node, as well as multiple performance objectives. It also captures decisions that span the entire SC simultaneously and, by implication, represents multiple network links. Using an industry test case, the paper demonstrates the effectiveness of the proposed framework in terms of fulfilling customer orders with lower production and emissions costs, compared to the results generated using existing tools.

Suggested Citation

  • Nagesh Shukla & Senevi Kiridena, 2016. "A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 6984-6996, December.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:23:p:6984-6996
    DOI: 10.1080/00207543.2016.1151567
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    References listed on IDEAS

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    Cited by:

    1. Abbas Mardani & Mehrbakhsh Nilashi & Jurgita Antucheviciene & Madjid Tavana & Romualdas Bausys & Othman Ibrahim, 2017. "Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature," Complexity, Hindawi, vol. 2017, pages 1-33, October.
    2. Anupam Keshari & Nishikant Mishra & Nagesh Shukla & Steve McGuire & Sangeeta Khorana, 2018. "Multiple order-up-to policy for mitigating bullwhip effect in supply chain network," Annals of Operations Research, Springer, vol. 269(1), pages 361-386, October.
    3. Benjamin Ohene Kwapong Baffoe & Wenping Luo, 2021. "South African Executives Propensity to Use, Diffuse, and Adopt the Humanitarian Logistics Digital Business Ecosystem (HLDBE)," SAGE Open, , vol. 11(3), pages 21582440211, September.
    4. Xu, Liming & Mak, Stephen & Brintrup, Alexandra, 2021. "Will bots take over the supply chain? Revisiting agent-based supply chain automation," International Journal of Production Economics, Elsevier, vol. 241(C).
    5. Michelle Dunbar & Simon Belieres & Nagesh Shukla & Mehrdad Amirghasemi & Pascal Perez & Nishikant Mishra, 2020. "A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network," Annals of Operations Research, Springer, vol. 290(1), pages 923-941, July.
    6. Lechtenberg, Sandra & Hellingrath, Bernd, 2021. "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers 37, University of Münster, European Research Center for Information Systems (ERCIS).
    7. Roberto Dominguez & Salvatore Cannella, 2020. "Insights on Multi-Agent Systems Applications for Supply Chain Management," Sustainability, MDPI, vol. 12(5), pages 1-13, March.
    8. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).

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