An Enhanced Ant Colony Algorithm-Based Low-Carbon Distribution Control Method for Logistics Leveraging Internet of Things (IoT)

4区 计算机科学 Q3 Engineering
You-wu Liu, Jun-long Li, Ming-yue Liu, Bian-bian Jiao
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引用次数: 0

Abstract

This paper presents a low-carbon vehicle routing optimization model to reduce energy consumption and carbon emissions in logistics and distribution. The model is solved using a hybrid algorithm of simulated annealing and ant colony optimization. It enhances the information pheromone concentration update process and directionality by introducing a carbon emission factor and a multifactor operator. Additionally, an adaptive elite individual reproduction strategy is employed to improve algorithm efficiency. In this case study focusing on cold chain logistics distribution, both the model and algorithm under consideration were evaluated. The findings affirm the effectiveness of the model in reducing carbon emissions and demonstrate the efficiency and robustness of the algorithm. Through this analysis, the paper sheds light on environmentally sustainable practices in logistics distribution.
基于增强蚁群算法的物联网物流低碳配送控制方法
为了降低物流配送过程中的能源消耗和碳排放,提出了一种低碳车辆路径优化模型。采用模拟退火和蚁群优化的混合算法对模型进行求解。通过引入碳排放因子和多因子算子,增强了信息信息素浓度更新的过程和方向性。此外,采用自适应精英个体繁殖策略提高算法效率。以冷链物流配送为例,对所考虑的模型和算法进行了评价。研究结果证实了该模型在减少碳排放方面的有效性,并证明了该算法的有效性和鲁棒性。通过这一分析,本文揭示了环境可持续的实践在物流配送。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
自引率
0.00%
发文量
2475
审稿时长
9.9 months
期刊介绍: Presenting comprehensive coverage of this fast moving field, Wireless Communications and Mobile Computing provides the R&D communities working in academia and the telecommunications and networking industries with a forum for sharing research and ideas. The convergence of wireless communications and mobile computing is bringing together two areas of immense growth and innovation. This is reflected throughout the journal by strongly focusing on new trends, developments, emerging technologies and new industrial standards.
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