The increasing frequency and severity of disruptions in global supply chains have underscored the need for resilient port operations. Ports represent critical nodes in the maritime–hinterland interface. Systemic risks arising from disruptive events like droughts, labor strikes, or demand fluctuations can propagate rapidly across interconnected transport networks. Existing research on port resilience largely focuses on isolated components of port operations or infrastructure, offering limited insights into systemic risks that span the entire system. As a result, the interplay between strategies, the adaptive role of stakeholders, and their systemic impacts on the whole supply chain remain insufficiently understood. This study addresses this gap by asking: How can systemic risks in port operations be minimized through resilience-based strategies, identified via a simulation of port disruptions and recovery?

To answer this question, a hybrid simulation approach is adopted, combining discrete event simulation (DES) and agent-based modeling (ABM) in the AnyLogic platform. DES captures port and terminal operations, including queuing dynamics, capacity constraints and resource allocation. ABM represents the adaptive decision-making of inland transport actors such as barge, truck, and rail operators. This integration allows for the simultaneous assessment of process-level efficiency and actor-level adaptability. The Port of Rotterdam serves as the case study, given its status as Europe’s largest multimodal hub and its relevance for resilience planning at the sea–port–land interface. Three disruption scenarios, namely a labor strike, drought affecting inland barge transport, and sea-side vessel demand fluctuations were modeled. Along with these distinct systemic risks, five resilience strategies were evaluated. These include autonomous facilities, dynamic rerouting, inland transshipment hub activation, capacity buffer expansion, and a collaborative strategy combining automation with rerouting. Performance was assessed using container throughput, delay costs, transport costs, delivery rate distributions, facility utilization and recovery time.

The results demonstrate that no single strategy is universally effective across all disruption types. Autonomous facilities enhanced throughput and reduced delays under capacity-limited disruptions such as labor strikes, but offered diminishing returns in the demand fluctuation scenario where a single transport mode becomes loaded too heavily. Dynamic rerouting and hub activation proved most effective minimizing delays in external disruptions such as drought and demand fluctuation, by enabling flows to bypass bottlenecks and stabilizing recovery times. Buffer expansion provided cost-effective shock absorption for short, localized events but lacked adaptability in more persistent disruptions. The collaborative strategy consistently outperformed individual measures, producing the lowest peak delays, fastest recovery times, and stable delivery distributions. However, it also pushed the system closer to full utilization, implying increased operational intensity and potential vulnerability under compounded shocks. These findings align with resilience literature distinguishing between absorptive capacity (buffers) and adaptive capacity (rerouting), while extending the discussion by showing that joint application of measures can generate synergistic resilience effects.

From a practical perspective, the analysis suggests that port authorities should prioritize automation to enhance internal robustness, while logistics providers and carriers benefit more from flexible routing options. The collaborative strategy demonstrates the value of coordinated investment across stakeholders, yet also highlights the importance of governance mechanisms to manage the risks of high-intensity operations. Cost reflections indicate that while automation requires large upfront investments, rerouting-heavy strategies may impose higher variable transport costs, making trade-offs between capital expenditure and operational flexibility central to decision-making.

This study contributes to resilience research by conducting systemic risk analysis through a hybrid ABM–DES model and by integrating both absorptive and adaptive strategies within a comparative framework. While results are calibrated to the Port of Rotterdam, the methodological approach and conceptual insights are generalizable to other large multimodal ports, albeit with performance outcomes contingent on local infrastructural and regulatory conditions. Limitations include simplified operational processes, underrepresentation of customs and scheduling constraints, and the absence of a full cost–benefit model. Future research should extend the simulation with predictive routing algorithms, incorporate detailed economic evaluation, and integrate stakeholder-driven scenario weighting.

Overall, the findings show that resilience in port–hinterland systems is multi-faceted: infrastructure-oriented measures provide robustness, routing measures deliver adaptability, and buffering offers low-cost stability. The combination of strategies, particularly automation and rerouting, can yield superior resilience outcomes but at the cost of higher operational intensity. These insights provide both theoretical advancement and practical guidance for designing resilience strategies that balance performance, cost-effectiveness, and systemic robustness in complex port networks.