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An optimization model with a lagrangian relaxation algorithm for artificial internet of things-enabled sustainable circular supply chain networks

Tavana, M ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1007/s10479-023-05219-3
  3. Publisher: Springer , 2023
  4. Abstract:
  5. Circular supply chain (CSC) networks improve sustainability and create socially responsible enterprises through recycling, harvesting, and refurbishing. This study develops a Lagrangian relaxation (LR) algorithm for solving location-inventory-routing (LIR) problems with heterogeneous vehicles in multi-period and multi-product sustainable CSC networks. The proposed Artificial Internet of Things (AIoT) enabled sustainable CSC is designed to increase network performance and create a secure and traceable environment. For the first time, an LR algorithm is proposed to solve the LIR problems in an AIoT-enabled CSC network with storage, backorder shortage, split-delivery, and time window potentials. Sixteen small- and medium-size simulated problems were produced to assess the performance of the proposed algorithm relative to the GAMS software. The results show the proposed algorithm can solve the small- and medium-size problems as effectively as GAMS software but faster and more efficiently. In addition, eight large-size simulation problems were produced and solved by the algorithm. While the GAMS software failed to solve the large-size problems, the LR algorithm solved them efficiently and successfully. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
  6. Keywords:
  7. Internet of things ; Lagrangian relaxation ; Location-inventory-routing problem ; Optimization model ; Sustainable circular supply chain
  8. Source: Annals of Operations Research ; 2023 ; 02545330 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10479-023-05219-3