Irregular external truck arrivals at a marine container terminal often leads to long queues at gates and substantial greenhouse gas emissions. To relieve gate congestion and reduce carbon emissions, a new truck arrival pattern called “vessel dependent time windows (VDTWs)” is proposed. A two-phase queuing model is established to describe the queuing process of trucks at gate and yard. An optimization model is established to assign time window and appointment quota for each vessel in a marine container terminal running a terminal appointment system (TAS) with VDTWs. The objective is to minimize the total carbon dioxide emissions of trucks and rubber-tired gantry cranes (RTGCs) during idling. The storage capacity constraints of each block and maximum queue length are also taken into consideration. A hybrid genetic algorithm based on simulated annealing is developed to solve the problem. Results based on numerical experiments demonstrate that this model can substantially reduce the waiting time of trucks at gate and yard and carbon dioxide emissions of trucks and RTGCs during idling.

Roll-on/Roll-off passenger vessels transporting electric vehicles (Ro-Ro EVs) face unique fire hazards, challenging traditional fire risk management strategies. This study integrates fault tree analysis (FTA) with Fuzzy Bayesian Network (FBN) to assess the fire risks of Ro-Ro EVs across the entire hazard chain. Given limited historical accident data, five experts familiar with the Shanghai Baoshan–Chongming ferry route refine fault tree models to visualize key fire hazard chain mechanisms and estimate risk probabilities. The FBN incorporates fault tree hierarchical structures, EV and Ro-Ro vessel-related risk factors, and applies a nine-level fuzzy scoring system to assess these risks. The FTA-FBN model offers a comprehensive framework for evaluating emerging fire risks specific to Ro-Ro EVs. Findings indicate that the highest risk occurs during the ignition phase. Primary triggers include external heat sources, improper vehicle securing, and vehicle collisions, leading to thermal runaway in lithium batteries. Failures in extinguishing and detecting lithium battery fires exacerbate fire spread. Effective fire compartmentalization and flammable material management are essential to prevent uncontrolled fires. Recommendations for fire prevention and control include shipboard battery level monitoring, charging restrictions, explosion-proof electrical installations, enhanced ventilation, lithium battery fire suppression systems, and vehicle securing.

Steel cargo vessel sinking accidents (SCVSA) threaten maritime safety and disrupt global steel supply chains. This study integrates interpretive structural modeling (ISM) and fuzzy Bayesian networks (FBN) to evaluate SCVSA risks across the incident lifecycle. ISM identifies hierarchical relationships among multifaceted risk factors. FBN assesses lifecycle risks using fuzzy scoring, modular nodes, and a hierarchical structure, with muti-source data drawn from accident reports, expert opinions, and research studies. Experts estimate probabilities based on observations and causal scenarios involving steel cargo vessels at Shanghai Port. The ISM–FBN framework visualizes hierarchical risk factors and incorporates uncertainty in the data and causal relationships through fuzzy scoring, structural updates, and probability learning. This approach provides a robust and adaptable tool for assessing SCVSA risks, advancing maritime risk assessment methodologies. Key findings identify advanced vessel age, severe weather and sea conditions, and inadequate regulatory oversight as primary root causes. Poor cargo loading and stowage practices are direct contributors. Intermediate risk factors from deeper to surface layers flow from shipping companies to crew and further to vessel and environmental conditions. Multi-stage risk factors include inadequate emergency responses and improper cargo securing. To mitigate these risks, actionable insights are provided, including fleet modernization, enhanced regulatory compliance, crew training, and improved emergency preparedness.