A Stochastic SEIQR-Based Model for Disruption Propagation and Resilience in Transport Logistics Networks

This paper presents a stochastic modelling framework based on the SEIQR epidemic model to analyse the propagation of disruptions and the dynamics of resilience within transport logistics networks. Each operational unit in the network—such as a warehouse, terminal, or hub—transitions through five distinct states: Susceptible, Exposed, Infected, Quarantined, and Recovered, reflecting varying levels of risk exposure and adaptive response. The model combines a non-monotone incidence with organisational constraints in the spread of the disruption and uses stochastic differential equations to describe inherent uncertainty in system dynamics. The proposed framework provides a versatile methodology for testing the robustness of logistics systems in the presence of uncertainty and for supporting the design of more resilient network structures. The model will be further validated by using empirical data in scenario-based simulations.