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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.

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