目的 通过构建多目标优化模型,从而确定最佳的回收中心选址方案,以解决循环包装箱回收中心的选址难题。方法 建立多目标优化模型,以达到最小化成本、碳排放和运输距离的目的。采用人工蜂群算法（ABC）对优化模型进行求解,并采用非支配排序遗传算法 II（NSGA-II）、粒子群优化算法（PSO）和蚁群算法（ACA）作为对比算法,对其在不同侧重点下的性能表现及所得的最优选址方案进行对比分析。结果 ABC算法在均衡性、效率以及全局最优解的搜索能力上均优于另外3种算法,它能够更有效地平衡多目标之间的冲突,从而得出更为理想的选址方案。结论 ABC算法为解决循环包装箱回收中心选址问题提供了更为合理、优越的解决方案,特别是在处理多目标优化问题时表现出色。
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.
关键词
循环包装 /
人工蜂群算法 /
回收中心 /
选址 /
多目标
Key words
returnable packaging /
artificial bee colony algorithm /
recycling center /
site selection /
multi-objective
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基金
北京市教委-市自然基金委联合资助项目（KZ202210015020）; 北京印刷学院科研平台建设项目（KYCPT202507）
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