With the COVID-19 outbreak hitting the world, the frequency and severity of port congestion caused by various factors are increasing, challenging the stability of international supply chains. Thus, it is necessary to conduct an in-depth study on congestion risks to reduce their adverse impacts on congestion. Although traditional criticality analysis techniques may be capable of ranking port congestion risk in common scenarios, new risk analysis methods are urgently required to tackle uncertainty along with the COVID-19 pandemic. This paper develops a methodology designed for the identification and prioritization of port congestion risk during the pandemic. First, a novel congestion risk assessment model is established by extending the risk prioritization index (RPI) suggested by failure mode and effects analysis (FMEA). Next, the combination of fuzzy Bayesian reasoning, AHP and the variation coefficient method is incorporated into the model in a complementary way to facilitate the treatment of uncertainty and quantitative analysis of the congestion under the different influence of risk factors in ports. Finally, the mode introduces a set of risk utility values for calculating the RPI for prioritization. A real case study and a sensitivity analysis were carried out to illustrate and validate the proposed model. The results proved that the applied method is feasible and functional. In the illustrative example, the top three risk factors are “Interruption of railways/barges services”, “Skilled labor shortage” and “Shortage of truck-drivers/drayage truck”. The findings obtained from this paper could provide useful insights for risk prevention and mitigation.

Transportation has the highest dependence on fossil fuels of any sector and accounts for 37% of carbon dioxide (CO₂) emissions. Maritime transportation is responsible for around 940 million tons of CO₂ and approximately 3% of global emissions annually. The significant increase in shipping activities around the globe has magnified the generation of toxic pollutants. In recent years, shipping emissions have received significant attention in developed countries due to global climate change, while in developing countries, researchers are making enormous efforts to tackle this catastrophic and pressing issue. This study considers Muhammad Bin Qasim Port (MBQP), Karachi, Pakistan as a case study. This study employed an activity-based or bottom-up approach with a standard procedure to estimate the various anthropogenic pollutants emissions including particular matters (PM₁₀ and PM_2.5), nitrogen oxide (NO_x), sulfur dioxide (SO₂), carbon monoxide (CO), CO₂, methane (CH₄), non-methane volatile organic compound (NMVOC), and hydrocarbon (HC) under different operational modes, i.e., hoteling, maneuvering, and reduced speed zones. The results indicated that CO₂ was the highest contributor with a proportion of 92%, NO_x 5%, and SO₂ 1.5% for all three operational modes. Moreover, the results indicated that container ships account for 64% of overall emissions, followed by tankers for 24%. Regarding the monthly trend, the findings revealed that November and December had the highest emission rates, with over 20% of the total emissions recorded. This study’s findings will assist stakeholders and policymakers to prioritize maritime emissions in developing countries.

Purpose: This paper aims to propose a technique based on cognitive assessments to quantify identified operational risks from the perspective of container shipping or logistics system administrators. The results derived from the risk quantification could be used to prioritize risks as well as support the decision-making process in risk prevention and mitigation. Design/methodology/approach: This paper identified container shipping operational risks (CSORs) from a logistics perspective. A multivariate risk evaluation mechanism by fuzzy rules Bayesian network (FRBN) was established. An improved two-level parameter set based on the failure mode and effects analysis (FMEA) was used to support the input extraction process. By feeding cognitive assessments into the model, the identified risks are evaluated based on their utility values. An illustration example and a sensitivity analysis were carried out to justify and validate the proposed model. Findings: The highest positions in the prioritized list of CSORs in the case study are dominated by risks in the physical flow with the first three are piracy and terrorism, force majeure and port congestion. The results derived from the case study with the satisfaction of all pre-defined axioms proved the feasibility and illustrated the functionality of the proposed risk assessment and prioritization technique. Originality/value: Controlling risk is irrefutably a significant issue of container shipping and logistics management because of the inconsistency of risk definitions and the involvement of uncertainties. The proposed risk evaluation mechanism and the identified list of CSORs could be beneficial in system management, decision-making and reliability performance.

Supplier selection in the shipbuilding industry is a typical multicriteria group decision-making (MCGDM) problem, often characterized by significant uncertainty and fuzziness. To address this issue effectively, this paper proposes a novel integrated approach for supplier selection in shipbuilding enterprises by combining intuitionistic fuzzy sets (IFSs) with the weighted aggregated sum product assessment (WASPAS) method. The proposed method utilizes IFS operators alongside an innovative process for evaluating indicator weights. Initially, an intuitionistic fuzzy number approach is employed to obtain indicator data, which effectively captures the uncertainty of linguistic variables and ensures accurate reflection of real-world conditions. Subsequently, the indicator weights are evaluated by integrating subjective weights, derived through the best–worst method, with objective weights, calculated using an entropy-based approach, resulting in more balanced and realistic weight assignments. Subsequently, the WASPAS method is used to prioritize alternative suppliers, and a shipbuilding enterprise in Shanghai is taken as an example to verify the effectiveness of the model. In addition, to evaluate the stability of the proposed method, sensitivity analyses were performed for varying attribute values. The results demonstrate that the combination of subjective and objective weights enhances the stability of the method under varying attribute weights. Finally, a comparison with various existing methods based on intuitionistic fuzzy information proves that the proposed method exhibits certain advantages in solving the MCGDM problem under uncertain environments.

As technical incentives, Ship Energy Efficiency Management Plan and Energy Efficiency Design Index, became the guidance for shipping companies in low-carbon shipping (LCS) integration. Despite the diversity of LCS measures, a mechanism to prioritize them is still urgently required from the view of shipping stakeholders. This paper developed a LCS measures prioritization mechanism for vessels relied on the combination of Fuzzy-based Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) and Fuzzy Quality Function Deployment (FQFD) so as to facilitate the treatment of uncertainties, thus realizing effective quantitative analysis of the LCS measures. Four performance aspects of LCS measures including ecological, reputation and competitive advantage, economic, and certainty level were analyzed and used for suitableness assessment, whereas weights of prioritizing criteria were determined by FQFD, alternatives were prioritized by FTOPSIS to build up a list of ranked LCS measures. The illustration example based on collected data of six alternatives was carried out to validate the feasibility of the methodology in prioritizing LCS measures. The outcomes can be used either as a stand-alone technique for prioritizing or as part of an integrated decision-making method for evaluating the effectiveness of chosen LCS measures.

Container ports are prone to delays, congestion, and logistics interruptions under the perturbation of uncertain events inside and outside the system. This not only affects the service quality of the system but also brings a serious blow to the whole transportation network. Therefore, this paper aims to develop a hybrid Bayesian network (BN) model to investigate the failure mechanism of the container port logistics system. Considering the complex coupling relationship between failure risks, the DEMATEL and ISM methods are presented to thoroughly analyze the interdependence and hierarchical structure of system failure factors. The failure evolution mechanism of the system is then analyzed using BN reasoning ability. The suggested hybrid model can identify the main failure factors, examine how factors are coupled, and produce the main propagation path resulting in system failure. The findings indicate that the risks associated with technology, facilities, and equipment are the most significant and immediate in the system; human risks affect all system components by acting on other factors; organizational management risks have a fundamental impact on the stability of the system; additionally, the uncertainty of external risks has greatly increased the variability of each logistics link. This study provides useful insights for port logistics risk management.