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Multimodal transportation, as an efficient and sustainable alternative to unimodal transportation, refers to the utilization of multiple modes, the utilization of standard loading units, and flexibility in planning. The complexity of multimodal transportation at the operational level lies in being able to deal with dynamic events that are unknown before their realization. However, stochastic information on some of the events might be available from historical data. This paper proposes an anticipatory optimization approach to handle dynamic shipment requests in multimodal transportation by incorporating stochastic information of requests’ origin, destination, volume, announce time, release time and due time. The experimental results show that the anticipatory approach outperforms a myopic approach in which decisions are made only based on deterministic information in reducing total costs under various scenarios of the multimodal matching system.

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Global intermodal transportation involves the movement of shipments between inland terminals located in different continents by using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of shipment requests with transport services in an integrated global network. The characteristics of the global intermodal shipment matching problem include acceptance and matching decisions, soft time windows, capacitated services, and transshipments between multimodal services. The objective of the problem is to maximize the total profits which consist of revenues, travel costs, transfer costs, storage costs, delay costs, and carbon tax. Travel time uncertainty has significant effects on the feasibility and profitability of matching plans. However, travel time uncertainty has not been considered in global intermodal transport yet leading to significant delays and infeasible transshipments. To fill in this gap, this paper proposes a chance-constrained programming model in which travel times are assumed stochastic. We conduct numerical experiments to validate the performance of the stochastic model in comparison to a deterministic model and a robust model. The experiment results show that the stochastic model outperforms the benchmarks in total profits.

This paper examines the development modes of inland ports based on the economic models and the Chinese empirical cases. After reviewing the recent policies in China, four modes, i.e., the government-driven mode, the seaport-driven, the market-driven mode and the corridor-effect mode, are established to describe the development of Chinese inland ports from the perspective of the driving forces. Moreover, we setup an economic model to compare them and conclude that (1) the seaport-driven mode promotes the larger inland port than the corridor-effect mode and the market-driven mode; (2) if the marginal capacity investment cost is low or the efficiency of the inland port is high enough, the corridor-effect mode leads to higher social welfare than the market-driven mode and the seaport-driven mode; (3) whether the government-driven mode promotes the larger inland port and higher social welfare than the other modes depends on the positive externality from the inland port to the social welfare; (4) The "Go west" policy and the Belt and Road Initiative (B&R) promote the inland port capacity under all modes. Whether the Free Trade Zone (FTZ) and the port integration promote the inland port capacity depends on the port efficiency improvement after the implementation of these policies.