Maritime transportation is responsible for the vast majority of global overseas commerce. However, systematic reviews of supply chain resilience from the maritime transportation perspective are limited. This study provides descriptive statistical analysis, bibliometric analysis, and literature review based on relevant papers from 2004 to 2022 to clarify developments in this field. Growth trends, research areas, research regions, influential journals, influential institutions, and highly cited articles are discussed in the statistical analysis. The bibliometric analysis includes collaboration networks, emerging trends, and co-citation analysis. Topical issues and methods in the field are discussed based on papers from 10 highlighted research clusters. Then, studies on resilience measurement, resilience evaluation, and resilience improvement in maritime transportation are reviewed and future directions are proposed. This study summarizes the emerging trends and developments of supply chain resilience from the maritime transportation perspective and supports the establishment of a more dependable maritime transportation system.

Learning-based optimization (LBO) algorithms have exhibited considerable advantages in solving routing problems. In this study, 831 papers published over two decades (2003–2024) are retrieved from the Web of Science database. This work aims to build extensive knowledge maps of LBO algorithms for routing problems by using a scientometric review of new developments and global trends. Prolific journals, conferences, authors, and institutions are discussed in the statistical analysis. The overall trend of LBO algorithms for routing problems is growing, and it is dominated by China and the USA. Collaboration network, co-citation analysis, and emerging trend analysis are developed to identify major disciplines of LBO algorithms for routing problems. Different emphases on the research field in operations research and computer science communities are identified respectively. Studies on LBO algorithms are reviewed from the perspectives of supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL). The major characteristics and limitations of LBO algorithms in each category are discussed. Dependence on sample labels and cluster numbers restricts the practical application of SL and UL to routing problems. Meanwhile, RL approaches, such as the deep Q-network, which exhibit fast convergence and computational efficiency, have elicited widespread attention in recent years. This study provides meaningful guidance and future direction to designing LBO algorithms for routing problems.