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Enhancing supply chain management in the physical internet: a hybrid SAGA approach

Scientific reports, 2023-12, Vol.13 (1), p.21470-21470, Article 21470 [Peer Reviewed Journal]

2023. The Author(s). ;The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-023-48384-y ;PMID: 38052833

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  • Title:
    Enhancing supply chain management in the physical internet: a hybrid SAGA approach
  • Author: Yan, Weiqi ; Li, Nan ; Zhang, Xin
  • Subjects: Adaptability ; Algorithms ; Collaboration ; Cost analysis ; Cost control ; Design ; Efficiency ; Internet ; Inventory ; Inventory control ; Inventory management ; Logistics ; Mathematical models ; Optimization algorithms ; Optimization techniques ; Order quantity ; Suppliers ; Supply chain management
  • Is Part Of: Scientific reports, 2023-12, Vol.13 (1), p.21470-21470, Article 21470
  • Description: This paper introduces an advanced inventory replenishment optimization approach tailored for the Physical Internet (PI), addressing the dynamic and complex nature of this environment. We propose a hybrid Simulated Annealing-Genetic Algorithm (SA-GA), engineered to optimize the balance between exploration and exploitation, ensuring adaptability and efficiency in a variety of PI contexts. The study also presents an enriched mathematical model integrating dynamic demand, and multi-objective optimization. The SA-GA algorithm emerges as a novel contribution, characterized by its computational efficiency and adaptability, marking an advancement in PI inventory management. The incorporation of real-time data analytics in our dynamic inventory replenishment strategy enhances adaptability and responsiveness, while the robust mathematical model offers a versatile tool for both theoretical analysis and practical application. Collectively, these innovations help bridge existing gaps in PI inventory management and serve as a reference for other similar studies.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-023-48384-y
    PMID: 38052833
  • Source: PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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