In the rapidly evolving global marketplace, the logistics sector faces a multitude of challenges that demand implementation of more resilient solutions to respond to any future disturbance. Synchromodal transport, which is viewed as an extension of multimodal transport, is known as a key answer to this issue, as it provides more flexible and sustainable freight transport and also focuses on collaboration between different logistics players. We consider synchromodal transport as a collection of agents that not only have their own characteristics and behaviors, but also interact with each other, which impacts the entire system. In this paper, we study the system using an Agent-Based Modeling approach. The network represents the combination of long-haul and drayage transport, where pre-haulage and end-haulage are done only by truck, and the rest can be done by trucks, trains, or barges. A numerical experiment is conducted to evaluate cost savings and emissions reduction under different logistics service providers’ relation and re-routing scenarios. Our findings show that synchromodal scenarios are more economically and environmentally efficient, and that they lead to higher flexibility and reliability compared to business-as-usual scenarios. Additionally, our model verifies that the cost saving is considerable when logistics service providers collaborate with each other. The results of sensitivity analyses show consistent overall trends when comparing the different scenarios. Therefore, the conclusions drawn from the original experiment appear to be applicable, not only for that specific instance, but have broader relevance and applicability.

Synchromodal transport implies using dynamically several modes of transport. Two relevant aspects of synchromodality are collaboration between actors and flexibility, aiming to make as many decisions as possible in real-time, imposing several changes on the logistic system's performance. This study aims to model the interactions between actors and their reactions to disruptions within the transport network. Using an agent-based modelling approach, the system is modelled as a collection of heterogeneous agents with complex interactions. Different collaboration scenarios and reaction strategies are modeled. The result shows improvement in the average cost, as the collaboration between logistics service providers and the flexibility of their response to disruptions increases. This model provides a testbed for stakeholders to assess different scenarios and ideas inexpensively and risk-free.