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首页> 外文期刊>Journal of Cleaner Production >A novel bi-objective credibility-based fuzzy model for municipal waste collection with hard time windows
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A novel bi-objective credibility-based fuzzy model for municipal waste collection with hard time windows

机译:基于小型的基于Bi-目标信誉的基于Bi-目标信誉的城市废物收集模糊模型,困难时间窗口

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摘要

In this study, a bi-objective vehicle routing mathematical model was proposed, in which the main objectives were minimizing the total economic cost, as well as the whole time for municipal waste collection. The considered waste collection network included bins, multiple depots, multiple heterogeneous vehicles, multiple intermediate facilities, and a landfill. Besides, in each day, municipal waste was collected several times in various hard time windows by heterogeneous vehicles, which all used vehicles could trip more than once per time window. It should be noted that the amount of waste generated was uncertain, which a fuzzy credibility theory was used to cope with this uncertainty. The exact solutions of some small problems were generated by the augmented epsilon-constraint method. Due to the complexity of this problem in more substantial sizes, the metaheuristic of Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used. Since the performance of metaheuristic algorithms is quite sensitive to their parameters, we used the self-adaptive method to tune the parameters of NSGA-II and also compared the results with the Taguchi method. The initial solutions of the metaheuristic approach were generated by a novel heuristic algorithm. Also, the waste collection vehicle routing problem of one of Tehran's regions was solved by the proposed model. After solving this model, the presented Pareto optimal solutions showed the total economic cost and the total time of the waste collection were improved by about 1.4% and 1.1%, respectively, which means a significant reduction, due to the high volume of costs and time. (C) 2021 Elsevier Ltd. All rights reserved.
机译:在本研究中,提出了一种双目标载体路线数学模型,其中主要目标是最大限度地减少经济总成本,以及市政废物收集的全部时间。考虑的废物收集网络包括箱,多个仓库,多个异质车辆,多个中间设施和垃圾填埋场。此外,在每天,由异构车辆在各种艰难时间窗口中收集了几次城市垃圾,所有使用的车辆每次窗口都可以多次绊倒。应该注意的是,产生的废物量不确定,模糊可信度理论用于应对这种不确定性。通过增强的epsilon-约束方法产生一些小问题的确切解。由于该问题的复杂性在更大的尺寸中,使用了非主导的分类遗传算法II(NSGA-II)的成群质训练。由于成群质识别算法的性能对其参数非常敏感,因此我们使用自适应方法调整NSGA-II的参数,并将结果与​​TAGUCHI方法进行比较。新型启发式算法生成了成群质方法的初始解。此外,德黑兰地区之一的废物收集车辆路由问题由所提出的模型解决。解决该模型后,呈现的Pareto最佳解决方案显示出总经济成本,废物收集的总时间分别提高了约1.4%和1.1%,这意味着由于成本高的成本和时间量大而显着减少。 (c)2021 elestvier有限公司保留所有权利。

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