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

Madjid Tavana (), Arash Khalili Nasr (), Francisco J. Santos-Arteaga (), Esmaeel Saberi () and Hassan Mina ()
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Madjid Tavana: La Salle University
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

Annals of Operations Research, 2024, vol. 342, issue 1, No 23, 767-802

Abstract: 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.

Keywords: Optimization model; Lagrangian relaxation; Sustainable circular supply chain; Internet of things; Location-inventory-routing problem (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05219-3

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