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首页> 外文期刊>Waste management & research >A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management
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A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management

机译：模糊需求下城市固体废物管理中的多程容量弧路由问题的混合增强蚁群算法

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摘要

Nowadays, urban solid waste management is one of the most crucial activities in municipalities and their affiliated organizations. It includes the processes of collection, transportation and disposal. These major operations require a large amount of resources and investments, which will always be subject to limitations. In this paper, a chance-constrained programming model based on fuzzy credibility theory is proposed for the multi-trip capacitated arc routing problem to cope with the uncertain nature of waste amount generated in urban areas with the aim of total cost minimization. To deal with the complexity of the problem and solve it efficiently, a hybrid augmented ant colony optimization algorithm is developed based on an improved max-min ant system with an innovative probability function and a simulated annealing algorithm. The performance of hybrid augmented ant colony optimization is enhanced by using the Taguchi parameter design method to adjust the parameters' values optimally. The overall efficiency of the algorithm is evaluated against other similar algorithms using well-known benchmarks. Finally, the applicability of the suggested methodology is tested on a real case study with a sensitivity analysis to evolve the managerial insights and decision aids.
机译：如今，城市固体废物管理已成为市政当局及其下属组织最重要的活动之一。它包括收集，运输和处置的过程。这些主要操作需要大量资源和投资，而这些资源和投资将始终受到限制。针对总行程费用最小化的城市垃圾产生量不确定性问题，提出了一种基于模糊可信度的机会约束规划模型。为了解决问题的复杂性并有效解决问题，基于具有创新概率函数和模拟退火算法的改进最大最小蚂蚁系统，开发了一种混合增强蚁群算法。通过使用Taguchi参数设计方法来最佳地调整参数值，可以增强杂交增强蚁群优化的性能。使用众所周知的基准，对照其他类似算法评估了该算法的整体效率。最后，在具有敏感性分析的真实案例研究中测试了所建议方法的适用性，以发展管理洞察力和决策辅助手段。

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