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Capacitated Facility Location and Allocation with Uncertain Demand for Tourism Logistics: A Multiobjective Optimisation Approach

机译:电容设施位置和分配对旅游物流需求不确定:多目标优化方法

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摘要

The paper develops a Multiobjective Optimisation (MOO) model for addressing Capacitated Facility Location Problem (CFLP) in tourism logistics, where two objectives are total of cost and customer service level. Nondominated Sorting Genetic Algorithm II (NSGA II) is used to solve the model. The illustrative case with imaginary data demonstrates that the model can figure out the location of the nodes of tourism logistics network and allocation of these sites, while the total of cost is reduced by up to 56.75% and customer service level is increased by an average of 105%. The distinction of this study compared to the current papers is that our model incorporates both items A and B to the subject matter of tourism logistics, where items A refer to tourism-related products and items B involve personal goods of tourists. The model established is limited with one assumption and one limitation which are associated with Vehicle Routing Problem (VRP) and the boundary of tourism logistics activity. Therefore, further research for the elimination of these limits is recommended.
机译:本文开发了一种多目标优化(MOO)模型,用于在旅游物流中寻址电容设施位置问题(CFLP),其中两个目标是成本和客户服务水平的总体。 NondoMinated分类遗传算法II(NSGA II)用于解决模型。虚拟数据的说明性案例表明,该模型可以弄清楚旅游物流网络节点的位置和这些网站的分配,而成本总额可降低56.75%,客户服务水平平均增加105%。与目前的论文相比,这项研究的区别于,我们的模型将项目A和B纳入旅游物流的主题,其中涉及与旅游相关的产品和项目涉及游客的个人商品。建立的模型受限于一个假设和与车辆路由问题(VRP)和旅游物流活动的边界相关的一个限制。因此,建议使用进一步研究消除这些限制。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第14期|4158940.1-4158940.18|共18页
  • 作者单位

    Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China|Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China|Hainan Policy & Ind Res Inst Low Carbon Econ Haikou Hainan Peoples R China;

    Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China|Hainan Policy & Ind Res Inst Low Carbon Econ Haikou Hainan Peoples R China|Univ Sussex Sch Business Management & Econ Brighton E Sussex England;

    Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China|Hainan Policy & Ind Res Inst Low Carbon Econ Haikou Hainan Peoples R China;

    Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China|Hainan Policy & Ind Res Inst Low Carbon Econ Haikou Hainan Peoples R China;

    Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China|Hainan Policy & Ind Res Inst Low Carbon Econ Haikou Hainan Peoples R China;

    Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China|Hainan Policy & Ind Res Inst Low Carbon Econ Haikou Hainan Peoples R China;

    Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China;

    Hainan Univ Sch Mech & Elect Engn Haikou Hainan Peoples R China|Hainan Policy & Ind Res Inst Low Carbon Econ Haikou Hainan Peoples R China;

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