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

Author

Listed:
  • Madjid Tavana

    (La Salle University
    University of Paderborn)

  • Arash Khalili Nasr

    (Sharif University of Technology)

  • Francisco J. Santos-Arteaga

    (Universidad Complutense de Madrid)

  • Esmaeel Saberi

    (Tarbiat Modares University)

  • Hassan Mina

    (Shanghai Maritime University)

Abstract

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.

Suggested Citation

  • Madjid Tavana & Arash Khalili Nasr & Francisco J. Santos-Arteaga & Esmaeel Saberi & Hassan Mina, 2024. "An optimization model with a lagrangian relaxation algorithm for artificial internet of things-enabled sustainable circular supply chain networks," Annals of Operations Research, Springer, vol. 342(1), pages 767-802, November.
    • Handle: RePEc:spr:annopr:v:342:y:2024:i:1:d:10.1007_s10479-023-05219-3
    DOI: 10.1007/s10479-023-05219-3
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