An in-depth metaheuristic approach to design a sustainable closed-loop agri-food supply chain network

Gholian-Jouybari, Fatemeh; Hajiaghaei-Keshteli, Mostafa; Smith, Neale R.; Calvo, Ericka Zulema Rodríguez; Mejía-Argueta, Christopher; Mosallanezhad, Behzad

doi:10.1016/j.asoc.2023.111017

Cited by 10 publications

(1 citation statement)

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“…Moreover, NSGA-II is also commonly used to solve transportation and routing problems [149][150][151]. NSGA-II is also an important tool in overcoming sustainability problems in the supply chain [152][153][154]. Therefore, in the future, various optimization techniques and metaheuristics can be integrated into genetic algorithms in logistics and supply chain management.…”

Section: Keyword Analysesmentioning

confidence: 99%

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

Lam,

Lee

2024

Mathematics

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As a famous population-based metaheuristic algorithm, a genetic algorithm can be used to overcome optimization complexities. A genetic algorithm adopts probabilistic transition rules and is suitable for parallelism, which makes this algorithm attractive in many areas, including the logistics and supply chain sector. To obtain a comprehensive understanding of the development in this area, this paper presents a bibliometric analysis on the application of a genetic algorithm in logistics and supply chains using data from 1991 to 2024 from the Web of Science database. The authors found a growing trend in the number of publications and citations over the years. This paper serves as an important reference to researchers by highlighting important research areas, such as multi-objective optimization, metaheuristics, sustainability issues in logistics, and machine learning integration. This bibliometric analysis also underlines the importance of Non-Dominated Sorting Genetic Algorithm II (NSGA-II), sustainability, machine learning, and variable neighborhood search in the application of a genetic algorithm in logistics and supply chains in the near future. The integration of a genetic algorithm with machine learning is also a potential research gap to be filled to overcome the limitations of genetic algorithms, such as the long computational time, difficulties in obtaining optimal solutions, and convergence issues for application in logistics and supply chains.

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