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Kotary, J., Fioretto, F., Van Hentenryck, P. and Wilder, B. (2021) End-to-End Constrained Optimization Learning: A Survey. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, 19-27 August 2021, 4475-4482.
https://doi.org/10.24963/ijcai.2021/610

has been cited by the following article:

• TITLE: A Review of Intelligent Optimization Algorithms in Supply Chain Networks

  AUTHORS: Nanlan Zhang, Fanrong Xie

  KEYWORDS: Supply Chain Networks, Intelligent Optimization Algorithm, Optimization Problem, Multi-Objective Optimization, Uncertainty Management

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.5, May 14, 2025

  ABSTRACT: Supply chain networks, which integrate nodes such as suppliers, manufacturers, and retailers to achieve efficient coordination and allocation of resources, serve as a critical component in enhancing corporate competitiveness. With the rapid advancement of artificial intelligence (AI) technologies, intelligent optimization algorithms have been increasingly applied in supply chain networks. This paper highlights classic algorithms such as genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO), and expounds improved versions of these algorithms. By analyzing algorithmic enhancement strategies, including quantum GA, dynamic pheromone update mechanisms on ACO, and adaptive inertia weight adjustments on PSO, the development trends of intelligent optimization algorithms are outlined.