Benchmarking container ports of different physical sizes and figuring out the relationship between size and efficiency is complex due to the heterogeneous environment. In dealing with heterogeneity, the selection bias is often overlooked. Therefore, this study proposes an integrated multivariate genetic matching and stochastic DEA algorithm to evaluate the efficiency of container ports. It paves the way for container ports that differ in a selected feature, such as size, to be benchmarked using well-balanced clusters. Thus, the port managers can identify the most similar-featured peers in benchmarking with DEA or alternative models to acquire robust estimates without dependence on a longitudinal data set. The results of the model applied to international container ports imply the increase in size impacts efficiency negatively, while connectivity does positively, which contradicts the commonly held perception of stakeholders that the larger the container ports, the more efficient. That is, well-managed small container ports can also be as efficient. Therefore, it is concluded that the results of integrating genetic matching into the performance measurement provide beneficial inferences. In future research, clustering the observed container ports according to a specific feature and balancing clusters with multivariate genetic matching can provide valuable insights into the industry.

中文翻译：

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集装箱港口效率和物理规模的变化：遗传匹配和随机数据包络分析的结合

由于异构环境，对不同物理规模的集装箱港口进行基准测试并找出规模和效率之间的关系非常复杂。在处理异质性时，选择偏差常常被忽视。因此，本研究提出一种综合多元遗传匹配和随机DEA算法来评估集装箱港口的效率。它为使用平衡良好的集群进行基准测试的选定功能（例如大小）不同的集装箱港口铺平了道路。因此，港口管理者可以通过 DEA 或替代模型进行基准测试来识别特征最相似的同行，以获得可靠的估计，而不依赖于纵向数据集。该模型应用于国际集装箱港口的结果表明，规模的增加对效率产生负面影响，而连通性则产生积极影响，这与利益相关者普遍认为的集装箱港口越大效率越高的看法相矛盾。也就是说，管理良好的小型集装箱港口也可以同样高效。因此，得出的结论是，将遗传匹配纳入性能测量的结果提供了有益的推论。在未来的研究中，根据特定特征对观察到的集装箱港口进行聚类，并通过多变量基因匹配平衡集群可以为该行业提供有价值的见解。