Driven by China’s booming natural gas consumption market, LNG (Liquified Natural Gas) shipping import has grown rapidly. To facilitate scientific and efficient decision making on LNG shipping fleet deployment and the development of the LNG shipping industry, this article proposes an optimization model to minimize annual fleet operating costs, including voyage cost, running cost, and capital cost. Under the consideration of the mixed factors of self-owned and time charter vessels, epidemic prevention and control, port congestion, transportation time cost, and evaporation loss, as well as navigation security and emergency situations, the validity and optimality of the model are demonstrated by the empirical example and the cost comparison between the conventional and optimized solution. The results show that this optimization model can reduce the total cost by 9.87%. Then, through sensitivity analysis, various significant factors affecting the operating costs of LNG shipping enterprises and their degrees of influence are determined. Based on the analysis of the relevant causes, some actionable countermeasures are recommended, including establishing a shipping price reciprocity mechanism and full chain investment planning, optimizing the inbound link to reduce invalid berthing time, strengthening the construction competitiveness and economy of scale of larger LNG ships, and building a combined dual resource pool transportation mode. This paper contributes to improving transregional maritime energy transport and management capacity, while further enhancing the energy security and development of port cities and their economic hinterlands.

The importance of inland ports in promoting current cross-border trade is increasingly recognized. In this work, we aim to design the entire network for the cross-border multimodal container transport system based on inland ports. Unlike previous studies, we consider strong uncertainty in cross-border transportation demand to be caused by a variety of realistic factors such as the global economic situation, trade policies among countries, and global epidemics, etc. To handle the demand uncertainty, we develop an uncertain programming model for the considered cross-border multimodal container transportation network design problem to minimize the expectation of the total costs, including carbon emissions, by imposing two types of chance constraints for capacity limitations. Under mild assumptions, we further convert the proposed uncertain model into its equivalent deterministic one, which can be solved by off-the-shelf solvers such as CPLEX, Gurobi, and Lingo. Finally, we illustrate the applicability of the proposed model by taking the Huaihai Economic Zone-Europe multimodal container transport system as a real-world case study. The computational results provide valuable suggestions and policy guidance regarding four issues: the inland port locations, the transportation route choices, the strategies for reducing the total cost, and the schemes for improving network performance against uncertain demand.

Inland ports are gaining more and more attention as important hubs for inland cities to promote foreign trade. However, studies on the evaluation of inland ports are lacking. In this work, we aim to construct an index system and propose a multi-criteria group decision-making method to comprehensively evaluate the development of inland ports. Unlike previous studies, using pressure–state–response model as a reference, we built up a demand–risk–power–potential framework for the index system proposed in this study. To determine the different weights for each indicator, which is a typical multi-criteria decision-making problem, we innovatively combined the decision-making trial and evaluation laboratory (DEMATEL) and the Bayesian best–worst method (BBWM) based on their distinct advantages in dealing with data coupling and group decision-making. In addition, this work introduces a case study of inland ports in the Huaihai Economy Zone to validate the efficacy of the proposed evaluation model and method. After calculating and obtaining the comprehensive scores and rankings of each inland port in this case, we compared the evaluation results with those under the BBWM, TOPSIS, and CRITIC methodologies, and found that the results under the DEMATEL–BBWM methodology can provide better differentiation for inland port evaluation results. Moreover, based on the evaluation results, a performance–importance matrix is formulated to identify the areas requiring attention in the development process of each inland port. Subsequently, rational managerial insights are put forward to achieve the sustainable development of inland ports in the Huaihai Economy Zone.

Transporting parcels on urban passenger rail transit is gaining growing interest as a response to the increasing demand and cost of urban parcel delivery. To analyze the welfare effects of different fare regimes when allowing parcel services on an urban rail transit, this paper models the optimal service problem where the transit operator chooses the number of trains and the departure intervals. By introducing a reduced form train timetable problem, the passenger train crowding model is extended to incorporate the effect of freight train scheduling. We show that the freight users are better off in the time-varying optimal fare regime, while passengers are worse off, and that the time-varying optimal fare regime calls for more trains than the optimal uniform fare regime. However, the reduction in passenger trains due to the introduction of freight service can eliminate the welfare gain from passenger time-varying fare. If the price elasticity of freight demand is relatively high, implementing road toll can generate welfare loss when rail transit is privately operated.

In the management of intermodal transportation, incentive contract design problem has significant impacts on the benefit of a multimodal transport operator (MTO). In this paper, we analyze a typical water-rail-road (WRR) intermodal transportation that is composed of three serial transportation stages: water, rail and road. In particular, the entire transportation process is planned, organized, and funded by an MTO that outsources the transportation task at each stage to independent carriers (subcontracts). Due to the variability of transportation conditions, the travel time of each transportation stage depending on the respective carrier’s effort level is unknown (asymmetric information) and characterized as an uncertain variable via the experts’ estimations. Considering the decentralized decision-making process, we interpret the incentive contract design problem for the WRR intermodal transportation as a Stackelberg game in which the risk-neutral MTO serves as the leader and the risk-averse carriers serve as the followers. Within the framework of uncertainty theory, we formulate an uncertain bi-level programming model for the incentive contract design problem under expectation and entropy decision criteria. Subsequently, we provide the analytical results of the proposed model and analyze the optimal time-based incentive contracts by developing a hybrid solution method which combines a decomposition approach and an iterative algorithm. Finally, we give a simulation example to investigate the impact of asymmetric information on the optimal time-based incentive contracts and to identify the value of information for WRR intermodal transportation.