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Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm

机译：基于遗传算法的动态环境中p-Mobile枢纽定位问题的数学建模

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摘要

In this study, a new mobile p-hub location problem in a dynamic environment is proposed, where there are mobile facilities inside hub nodes that can be transferred to other nodes in the next period. Mobile facilities have a mobility feature and can be transferred to other nodes in order to meet demand. Using such facilities will save extra hub establishment and closing costs in networks. This approach can be used in some real-world applications with rapidly changing demand, such as mobile post offices or emergency medical service centers, because designing immobile hub networks may be less efficient. In addition, designing dynamic hub networks entails establishing and closing costs in different periods. The model also considers a mobility infrastructure of hub facilities. The numerical examples confirm that a mobile hub network is more efficient than an immobile hub network in a dynamic environment The effect of different parameters on the model is analyzed to consider its applicability conditions. A genetic algorithm, along with tuned parameters and a simulated annealing algorithm, are proposed to solve the model in large instances. Proposing of a model considering mobility feature in the hub location networks, proving its efficiency and finally proposing a proper solution algorithm are main contributions of this study. The model and solutions algorithms were analyzed by more numerical instances using Australia post (AP) dataset.
机译：在这项研究中，提出了一个在动态环境中的新移动p-hub定位问题，其中在集线器节点内有移动设施，可以在下一个时期转移到其他节点。移动设施具有移动性功能，可以转移到其他节点以满足需求。使用此类设施将节省额外的集线器建立和网络关闭费用。这种方法可用于需求迅速变化的某些实际应用中，例如移动邮局或急诊医疗服务中心，因为设计固定式集线器网络的效率可能较低。另外，设计动态集线器网络需要在不同时期建立和关闭成本。该模型还考虑了枢纽设施的移动基础设施。数值示例证实了在动态环境中移动集线器网络比固定集线器网络更有效。分析了不同参数对模型的影响，以考虑其适用条件。提出了遗传算法，调整后的参数和模拟退火算法来求解大型实例模型。提出一种考虑枢纽定位网络中的移动性特征的模型，证明其效率并最终提出一种合适的求解算法是本研究的主要贡献。使用澳大利亚邮政（AP）数据集，通过更多数值实例分析了模型和解决方案算法。

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  • 来源
    《Applied Mathematical Modelling》 |2018年第2期|151-169|共19页
  • 作者

    Mahdi Bashiri; Mohammad Rezanezhad; Reza Tavakkoli-Moghaddam; Hamid Hasanzadeh;

  • 作者单位

    Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran,School of Science, RMIT University, Melbourne, Australia;

    Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran;

    School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran,Universal Scientific Education and Research Network (USERN), Tehran, Iran,LCFC, Arts et Metiers Paris Tech, Metz, France;

    Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Mobile hub location; Dynamic environment; Mobility infrastructure; Genetic algorithm; Greedy local search;

    机译：移动中心位置;动态环境;移动基础设施;遗传算法贪婪的本地搜索;

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