Profit distribution plays an important role in the sustainable and stable development of liner alliances, this paper tries to solve the profit distribution issues in the liner alliance based on Shapley Value Method. Meanwhile, seeing that there is little consideration from the customer satisfaction, this paper establishes a new model by revising Shapley Value Method to distribute the profit of liner alliances from the perspectives of suppliers and customers and carry out verification through case analysis. The profit distribution method proposed in the paper is helpful to the reasonable profit distribution of liner alliance. It ensures the continuity and stability of liner alliance and provides a scientific decision-making basis for the profit distribution of liner alliance.

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This paper proposed improved measures for the shortest path fare scheme of urban rail transit. Firstly, this paper simulated Beijing rail transit by using Anylogic simulation technology and shortest path algorithm. Then, in order to find the travel time between any originations and destinations, this research measured the inbound time, waiting time, interval time, section running time, transfer time and outbound time. In addition, this paper used big data analysis technology to obtain the actual travel time distribution between any originations and destinations by processing the basic data of passengers entering and leaving the station. Finally, by comparing the valid path travel time calculated by any originations and destinations with the actual travel time distribution of passengers, the path taken by majority of passengers was pushed back to determine the ticket price based on the mileage of the path taken by the majority of passengers. The results reduced the dependence on government subsidies by rail transit operation and made up for the operation and maintenance costs. INDEX TERMS Shortest path, Anylogic simulation, travel time, time distribution, big data analysis, pricing scheme of urban rail transit.

The intelligent transformation of logistics plays a significant role in meeting the diverse needs of customers, improving operational efficiency, and reducing carbon emissions in logistics activities. Therefore, to achieve sustainable development, logistics enterprises need to face the decision-making problem of intelligent logistics transformation. In this paper, we construct a Stackelberg game model between a financially constrained logistics-service provider (LSP) and a well-funded logistics-service integrator (LSI) and discuss the impact of the wholesale price contract, the cost-sharing contract, the revenue-sharing contract, the two-part tariff contract, and the hybrid cost-sharing and revenue-sharing contract on the intelligence level of logistics services, the profits of supply-chain members, and the channel for logistics-service demand. We found that the cost-sharing contract and the revenue-sharing contract cannot achieve Pareto improvement in the profits of supply-chain members. In addition, the increase in bank-loan interest rates would seriously weaken the level of intelligence and market demand for the entire logistics service. However, when consumers do not have high requirements for the intelligence of logistics services, the two-part power–price contract can create a win–win situation for supply-chain members and increase market demand within a certain range; on the contrary, a hybrid contract of cost sharing and revenue sharing is the best choice. Moreover, in the process of contract design for the intelligent transformation of logistics services, it is necessary to pay attention to the influence of the price-sensitivity coefficient on decision-making.

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