With the global economy’s relentless growth and heightened environmental consciousness, sustainable maritime transport emerges as a pivotal development trajectory for the shipping sector. This study systematically analyzes 478 publications searched in the Web of Science Core Collection, from 2000 to 2023, utilizing bibliometric methods to investigate the application areas in sustainable development within the shipping industry. This study begins with an analysis of annual publication trends, which reveals a substantial expansion in research endeavors within this discipline over recent years. Subsequently, a comprehensive statistical evaluation of scholarly journals and a collaborative network assessment are conducted to pinpoint the foremost productive journals, nations, organizations, and individual researchers. Furthermore, a keyword co-occurrence methodology is applied to delineate the core research themes and emerging focal points within this domain, thereby outlining potential research directions for future research. In addition, drawing on the keyword co-occurrence analysis, the advancements in intelligent shipping technologies and green port construction applications within sustainable maritime transport are discussed. Finally, the review discusses the existing challenges and opportunities of sustainable maritime transport from a theoretical and practical perspective. The research shows that, in terms of intelligent shipping technology, data security and multi-source data are the focus that people need to pay attention to in the future; a trajectory prediction for different climates and different ship types is also an area for future research. In terms of green ports, Cold Ironing (CI) is one of the key points of the green port strategy, and how to drive stakeholders to build sustainable green ports efficiently and economically is the future developmental direction. This review serves to enhance researchers’ comprehension of the current landscape and progression trajectory of intelligent shipping technologies, thereby fostering the continued advancement and exploration in this vital domain.

This study pioneers the integration of large language models (LLMs) into simulation-based inventory optimization under supply chain disruptions. We develop and compare three human–AI collaborative approaches with varying degrees of automation: fully automated direct optimization (FADO), two-stage automated simulation and optimization (TASO), and simulation-based human-specified optimization (SHSO). Through extensive numerical experiments, we evaluate their performance using the probability of correct selection and solution quality under different operational conditions. Results show that while FADO offers the greatest automation and ease of implementation, its accuracy and stability are limited. TASO enhances computational robustness but remains sensitive to the granularity of parameters. SHSO consistently achieves statistically optimal and stable outcomes by combining LLM-driven simulation with human-specified algorithms. These findings advance the theoretical understanding of human–AI collaboration in operations and provide actionable guidance for managers seeking to enhance decision reliability and supply chain resilience.

1. In many real-world pricing scenarios, sellers face ambiguity (or Knightian uncertainty; see Knight, 2012) rather than risk. While risk refers to uncertainty with a known or estimable probability...

Restaurants are increasingly providing both dine-in and delivery services through online delivery platforms, yet how this prevalent strategy is affected by heterogeneous network externalities under competition remains understudied. This paper examines when a restaurant should introduce meal delivery service in this context by developing game-theoretic models with geographically distributed consumers and competing restaurants. We distinguish the intra-channel externality among dine-in consumers from the inter-channel externality generated by shared service resources. We first find that providing delivery service can increase a monopolistic restaurant’s profit through market expansion and coordinated pricing. When two offline restaurants compete in a duopoly, one restaurant’s adoption of delivery service transforms the market landscape and intensifies inter-firm competition, and the omnichannel restaurant’s prices and profit may become U-shaped in the intra-channel externality. Numerical comparisons suggest that the focal restaurant’s incremental profit can be larger when the intra-channel externality is relatively low or high, or when more potential consumers are informed of the restaurant’s presence. These benefits can spill over to the competing rival and consumers, creating all-win outcomes. When one offline restaurant competes with an online-only rival, the two restaurants’ profits are affected asymmetrically by the externalities under cross-channel competition, but they vary in the same direction after the focal restaurant adopts delivery service. It is more often observed that the focal restaurant’s profit improvement under the omnichannel strategy then depends on the strength of the inter-channel externality and reaches its highest level at an intermediate level of this externality. These findings reveal the distinct roles of heterogeneous network externalities in shaping firms’ pricing strategies, payoffs, and channel configurations in omnichannel restaurant service systems.