| Summary: | The increasing concern on global warming is prompting transportation sector to take into account more sustainable operation strategies. Among them, intermodal transportation (IT) has already been regarded as one of the most effective measures on carbon reductions. This paper focuses on the model and algorithm for a certain kind of IT, namely multi-objective multi-sourcing intermodal transportation network design problem (MO_MITNDP), in which carbon emission factors are specially considered. The MO_MITNDP is concerned with determining optimal transportation routes and modes for a series of freight provided by multiple sourcing places to find good balance between the total costs and time efficiencies. First, we establish a multi-objective integer programming model to formulate the MO_MITNDP with total cost (TTC) and maximum flow time (MFT) criteria. Specifically, carbon emission costs distinguished by the different transportation mode and route are included in the cost function. Second, to solve the MO_MITNDP, a hybrid estimation of distribution algorithm (HEDA) combined with a heterogeneous marginal distribution and a multi-objective local search is proposed, in which the from the Pareto dominance scenario. Finally, based on randomly generated data and a real-life case study of Jilin Petrochemical Company (JPC), China, simulation experiments and comparisons are carried out to demonstrate the effectiveness and application value of the proposed HEDA.
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