Integrated Synchromodal Transport Planning and Preference Learning

Abstract

A comprehensive understanding of shippers’ preferences can help transport freight forwarders create targeted transport services and enhance long-term business relationships. This research proposes an integrated approach to learn shippers’ preferences in synchromodal transport operations and optimize transport services accordingly. A preference learning method was developed to capture shippers’ preferences through pairwise comparisons of transport plans. To model the underlying complex nonlinear relationships and detect heterogeneity in preferences, artificial neural networks (NNs) were employed to approximate shippers’ utility for a specific plan. Leveraging the learned preferences, a synchromodal transport planning model with shippers’ preferences (STPM-SP) was proposed, with the objectives of minimizing the total transportation cost and maximizing shippers’ satisfaction. A case study based on the European Rhine-Alpine corridor was conducted to demonstrate the feasibility and effectiveness of the proposed approach. The results demonstrated that artificial NNs have the capacity to identify complex (i.e., nonlinear and heterogeneous) relationships in shippers’ preferences. The planning results showed that the STPM-SP effectively found solutions with a significant satisfaction improvement of 37%. This research contributes to learning shippers’ preferences in the transport operation process and highlights the importance of incorporating these preferences into the decision-making process of synchromodal transport planning.