Background: Logistics is vital for the trades of countries. The inputs such as raw materials and energy that is needed for production and also the outputs of these processes are transported and distributed effectively as a result of an efficient logistics process. In order to measure the logistics performance of countries, The World Bank (WB) is publishing an index entitled Logistics Performance for every two years. Methods: The main value of this study is to provide logistics performance scores of the selected countries for a selected time period. Thus, periodic evaluations can be done for a selected time period. The grey numbers are used for determining a new dataset for a time period and implement to Complex Proportional Assessment of Alternatives (COPRAS) method. 28 European Union (EU) member states plus 5 EU Candidate Countries are ranked by using the COPRAS-Grey (COPRAS-G) method according to their logistics performance scores. In order to see if the ranking calculated by COPRAS-G is representing the past index data, the bilateral comparisons of the rankings are investigated by using the Spearman Rank and Kendall’s Tau Correlation methods. Results: The results showed that the dataset obtained by using grey numbers represent the LPI scores of the countries for the selected time period. Although there are slight differences between the Spearman and Kendall correlation coefficients, the ultimate result is the same. The ranking calculated by COPRAS-G has the strongest relationship with all rankings published by WB. Conclusions: By using the grey numbers combined with the COPRAS-G method, the LPI of Countries can be evaluated for a time period.

中文翻译：

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基于时间段的COPRAS-G方法：在物流绩效指标上的应用

背景：物流对于国家贸易至关重要。生产过程所需的原材料和能源等输入以及这些过程的输出可通过有效的物流过程得到有效地运输和分配。为了衡量国家的物流绩效，世界银行（WB）每两年发布一次名为“物流绩效”的索引。方法：这项研究的主要价值是提供选定时间段内选定国家的物流绩效评分。因此，可以在选定的时间段内进行定期评估。灰色数字用于确定一个时间段内的新数据集，并用于替代物的复杂比例评估（COPRAS）方法。根据其物流绩效得分，使用COPRAS-Grey（COPRAS-G）方法对28个欧盟（EU）成员国和5个欧盟候选国家/地区进行排名。为了查看由COPRAS-G计算的排名是否代表过去的索引数据，使用Spearman排名和Kendall的Tau相关方法研究了排名的双边比较。结果：结果表明，使用灰色数字获得的数据集代表所选时间段内各个国家的LPI得分。尽管Spearman和Kendall相关系数之间存在细微差异，但最终结果是相同的。COPRAS-G计算的排名与世界银行发布的所有排名之间关系最密切。结论：通过结合使用灰色数字和COPRAS-G方法，