Multi-objective Site Selection of Circular Packaging Recycling Centers Based on Artificial Bee Colony Algorithm

LIU Xiao; ZHU Lei; ZHANG Yuan

doi:10.19554/j.cnki.1001-3563.2025.11.029

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (11) : 267-276. DOI: 10.19554/j.cnki.1001-3563.2025.11.029

Green Packaging and Circular Economy

Multi-objective Site Selection of Circular Packaging Recycling Centers Based on Artificial Bee Colony Algorithm

LIU Xiao, ZHU Lei, ZHANG Yuan

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Abstract

The work aims to determine the optimal site selection scheme for recycling centers dedicated to reusable packaging boxes by constructing a multi-objective optimization model. The model was developed to minimize cost, carbon emissions, and transportation distance. The Artificial Bee Colony (ABC) algorithm was employed to solve the optimization model, while the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Ant Colony Algorithm (ACA) were used as benchmark algorithms. A comparative analysis was conducted to evaluate the performance of these algorithms under different priorities and to assess the quality of the resulting site selection schemes. The experimental results demonstrated that the ABC algorithm outperformed the other three algorithms in terms of balance, efficiency, and global search capability. It was more effective in handling trade-offs among multiple objectives, thereby yielding more desirable site selection schemes. The ABC algorithm provides a more rational and superior approach for solving the site selection problem of recycling centers for reusable packaging, particularly excelling in addressing multi-objective optimization challenges.

Key words

returnable packaging / artificial bee colony algorithm / recycling center / site selection / multi-objective

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LIU Xiao, ZHU Lei, ZHANG Yuan. Multi-objective Site Selection of Circular Packaging Recycling Centers Based on Artificial Bee Colony Algorithm[J]. Packaging Engineering. 2025, 46(11): 267-276 https://doi.org/10.19554/j.cnki.1001-3563.2025.11.029

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