Exploring Synchromodal Transport Strategies Using Agent-Based Simulation

Synchromodal Transport (ST), known as synchronized multimodal transport, is a novel concept that involves using different modes to deliver freights from their origins to their destinations. ST aims to optimize the transport process, making it more sustainable, reliable, and efficient. However, although ST has many benefits, its implementation by companies still seems far away. There is still the lack of a mechanism that describes the current and future state of the system and allows the decision-makers to plan while staying flexible in their decisions. In this work, we create a (virtual) simulation model that represents the physical system and helps to understand and analyze it. This paper presents an agent-based simulation model (ABM) for ST planning, focusing on re-planning under disruptions. Using ABM, we study the behavior of multiple actors involved in a ST system, their interactions, and the impact of their behavior on the entire network.
The model studies a regional ST network at the operational level and from the Logistics Service Providers’ (LSPs') perspective. It represents the movement of vehicles and cargo within a GIS environment and considers trucks, trains, and barges as the available modes. The main agent types in our model are nodes (origins, destinations, terminals, depots), vehicles (trains, trucks, barges), services (trains services, barge services), orders, and LSPs. The overall dynamics of the system then emerge from the interactions of these agents. Within the model, agents are equipped with optimizations and decision-making algorithms allowing them to choose the best synchromodal route in response to disruptions or pulsations in demand. The model aims to minimize transportation costs, reduce carbon emissions, and ensure reliability.
A numerical experiment is conducted in the Benelux region (Belgium, Netherlands, Luxemburg) to evaluate cost savings and emissions reduction, considering different collaboration and re-routing strategies. We compare ST with traditional transport planning to assess the flexibility and reliability in the two cases. Moreover, we study different strategies of relations between LSPs. The results show that synchromodal scenarios lead to a 15% cost reduction and create 6% lower CO2 emissions. Moreover, synchromodal scenarios lead to higher flexibility and reliability than traditional scenarios. The model also verifies that the cost saving is considerable when LSPs collaborate rather than compete.