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Power Battery Recycling Mode Selection Using an Extended MULTIMOORA Method

机译:使用扩展的MULTIMOORA方法选择动力电池回收模式

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摘要

In order to improve the efficiency of the recycling of the electric vehicle power batteries and reduce the recycling cost, it is of great importance to select an optimal power battery recycling mode. In this paper, an extended MULTIMOORA (Multiobjective Optimization by Ratio Analysis plus full Multiplicative form) approach which combines with the two-dimension uncertain linguistic variables (TDULVs) and the regret theory, called TDUL-RT-MULTIMOORA method, is developed for solving the power battery recycling mode decision-making (PBRMDM) problem. Firstly, the evaluations of the power battery recycling modes over criteria are given by the experts using the TDULVs, and the evaluations of all experts are aggregated into a group linguistic decision matrix by the TDULDWA operator. On the basis of the regret theory, the perceived utility decision matrix is constructed. And then, in order to avoid the disadvantages of the subjective weighting methods, such as the deviation from the measured data and the dependence on the experience and knowledge of the experts, an objective entropy weighting method is applied. After that, the MULTIMOORA method is introduced to rank the power battery recycling modes. In the end, an illustrative example is given to verify the effectiveness and practicability of the proposed method.
机译:为了提高电动汽车动力电池的回收效率并降低回收成本,选择最佳的动力电池回收模式具有重要意义。本文针对二维不确定语言变量(TDULV)和后悔理论(TDUL-RT-MULTIMOORA方法),开发了一种扩展的MULTIMOORA(比率分析多目标优化加完全可乘形式)方法,结合了二维不确定语言变量(TDULV)和后悔理论。动力电池回收模式决策(PBRMDM)问题。首先,由专家使用TDULV进行对动力电池回收模式的评估,然后由TDULDWA运营商将所有专家的评估汇总到一组语言决策矩阵中。基于后悔理论,构造了感知效用决策矩阵。然后,为了避免主观加权方法的缺点,例如与测量数据的偏差以及对专家经验和知识的依赖,采用了一种客观的熵加权方法。之后,引入MULTIMOORA方法对动力电池回收模式进行排序。最后,给出了一个实例来验证所提方法的有效性和实用性。

著录项

  • 来源
    《Scientific programming》 |2018年第2期|7675094.1-7675094.14|共14页
  • 作者

    Ding Xuefeng; Zhong Junhui;

  • 作者单位

    Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;

    Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;

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

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