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An optimization model with a lagrangian relaxation algorithm for artificial internet of things-enabled sustainable circular supply chain networks.
- Source :
-
Annals of Operations Research . Mar2023, p1-36. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02545330
- Database :
- Academic Search Index
- Journal :
- Annals of Operations Research
- Publication Type :
- Academic Journal
- Accession number :
- 162191172
- Full Text :
- https://doi.org/10.1007/s10479-023-05219-3