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Integrating multi-stage data envelopment analysis and a fuzzy analytical hierarchical process to evaluate the efficiency of major global liner shipping companies

机译:集成多阶段数据包络分析和模糊层次分析法来评估主要的全球班轮运输公司的效率

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In this study, we establish a multi-stage data envelopment analysis model to evaluate the efficiency of global liner shipping companies (LSCs). Because conventional solution procedures cannot guarantee the uniqueness of solutions, in this study, we specifically devise a new two-phase algorithm to overcome this problem. The first phase ranks the priority of all stages by applying fuzzy analytical hierarchical process. The second phase then solves the efficiency score for each stage according to its priority. We established and empirically tested a three-stage research model based on data collected from the Containerization International Year Book, the Alphaliner website and annual LSC reports for the year 2012. The results show that the proposed algorithm not only determines unique solutions for the efficiency scores but also determines the priority order of the stages involved in this process. Taking advantage of the proposed model and algorithm, LSCs can effectively locate bottlenecks in their production processes and further improve them by adjusting the values of the corresponding input and output variables. In addition, the priority order of the stages obtained from the empirical study can also help LSCs allocate their resources.
机译:在这项研究中,我们建立了一个多阶段的数据包络分析模型来评估全球班轮运输公司(LSC)的效率。由于传统的求解程序无法保证求解的唯一性,因此在本研究中,我们专门设计了一种新的两阶段算法来克服此问题。第一阶段通过应用模糊分析层次过程对所有阶段的优先级进行排序。然后,第二阶段根据每个阶段的优先级解决效率得分。我们基于从《集装箱化国际年鉴》,Alphaliner网站和2012年LSC年度报告中收集的数据,建立了一个三阶段研究模型,并进行了实证测试。结果表明,该算法不仅为效率得分确定了唯一的解决方案而且还确定了此过程涉及的阶段的优先级顺序。利用所提出的模型和算法,LSC可以有效地定位其生产过程中的瓶颈,并通过调整相应的输入和输出变量的值来进一步改善它们。此外,从实证研究获得的阶段的优先顺序也可以帮助LSC分配资源。

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