It has been proven that exploring how to achieve an efficient transportation system is a crucial component of every sustainable transportation study. Rail-water intermodal transportation is recognized as one of the future transportation methods for being efficient, economical and environmentally friendly. To improve the efficiency, reduce transportation costs and maximize the resource utilization of outbound intermodal container transportation, based on the relationship between the container central station and the port station in the actual problems, the organization of railway container transportation was studied. A multi-objective optimization model was established in order to minimize the total cost in the process of transportation, which means maximizing the resource utilization and ensuring it is environmentally friendly. Additionally, an improved genetic algorithm (GA) was developed to solve the model. The calculation results of the model are obtained by the simulation calculation. The comparison with the conventional fixed axis transportation organization method proves that the model and algorithm can reduce costs by up to 24.57%. The result also shows that the container transport organization should be tried to satisfy the direct loading and discharging condition of “train-ship,” meanwhile reducing the storage time at the high toll central station. In conclusion, the model and algorithm are feasible and effective. Due to the universality of the model, it can be easily used and generalized in or out of China.

Last-train timetable coordination is extremely complex because a number of transfer directions involve in the subway network. In this paper, transfer redundant time (TRT) and transfer binary variables (TBV) that affect transfer results are considered in the Markowitz mean-variance model. By adjusting running time and dwelling time, the model creates a high-quality timetable that greatly improves the efficiency of transferring passengers. Furthermore, a genetic simulated annealing (GSA) algorithm is developed to solve this problem in the Beijing subway network, which consists of 14 lines, 17 transfer stations, and 42 key directions. The present model increases the number of successful connections by 40.0% and reduces the amount of just-missed connections by 83.3%, respectively. In addition, the mean-variance model significantly improves the subway network accessibility compared with the current practice of the last-train timetable.

This paper develops a game-theoretic model to analyze the competition between two container freight transportation modes (shipping and railway) using competitive game strategic interactions method, namely, deterrence, by taking account of the most cost-effective scale of the transportation capacity settings. The competition was set against the background of China’s Belt and Road (B&R) Initiative as a new situation for intercontinental Sino-Europe container freight transportation. The behavior of each mode (modeled as a carrier, resp.) is characterized by an optimization model with the objective of minimizing its cost by setting optimal basic freight rate and transportation deployment. A firm can use this method to compare the difference in the time value of the cargos and reduce the expense during the whole transportation process. Finally, the developed model is numerically evaluated by a case study of intercontinental transportation between Hefei (China) and Hamburg (Germany). The results show that deterrence effects largely depend on the deterrence objective, and the differential in the cost of two transportation modes tends to be stable with higher values in the deterrence objective. In the new intercontinental circumstance, the mode of railway transportation provides a new way to transport the cargos between China and Europe.

In this paper, the integrated scheduling of handling equipment at the railway handling area in container terminals is studied, where rail-mounted gantry cranes, internal trucks, and reach stackers are deployed. In the course of the handling operation, loading and unloading containers are handled simultaneously. The handling process is first studied and some scheduling schemes are put forward. Based on the analysis, the problem is formulated as a mixed-integer programming model, with the objectives of minimizing the makespan and the total waiting time of all equipment. Then, to solve the problem, a genetic algorithm is employed, where the first available machine rule is applied in the selection of trucks and reach stackers. Sets of numerical experiments are conducted to verify the effect of the proposed algorithm. Based on the results of experiments, some key indicators are calculated and the effects of different equipment configuration schemes are studied.

To develop low-carbon transport and promote sustainable economic development, this paper took the uncertainty in highway transport speed and transshipment time into account in the actual transport process and established multi-objective path-decision models of multimodal transport under different carbon policies. The expectation values of nonlinear uncertainties were estimated by Law of Large Numbers (LLN), and the models were solved by the K-shortest paths algorithm and non-dominated sorting algorithm (NSGA-II), whose advancement and effectiveness were verified through the comparison of SPEA2. Based on the Pareto theory, the optimally symmetrical compromise between the objectives and the influence of the transport speed uncertainty and carbon emission policies on path decisions were quantified and discussed. Taking the multimodal transport network of West Africa as the experimental background, the practicability of the path-decision results is analyzed, and a trade-off analysis is also conducted to provide the theoretical foundation for future freight transport planning.

Rail–truck intermodal terminals are an important type of dry port and play a vital role in inland freight transport. This paper addresses the container loading problem in rail–truck intermodal terminals considering energy consumption under the sustainability concept. We analyze the effect factors of energy efficiency for container loading operations and develop an optimization model to minimize the total handling time and container reshuffling. A genetic algorithm is designed to obtain the optimal container loading sequence. Computational experiments on a specific Chinese rail–truck intermodal terminal were conducted to evaluate the performance of our approach. Results show our approach has a good performance for different sizes, and the total handing time, reshuffling times and energy consumption of the handling task are prominently decreased.