Maritime transport systems represent complex socio-technical environments characterised by strong interdependencies between technical, organisational, environmental, and human subsystems, as well as by pronounced uncertainty and dynamic behaviour. Traditional deterministic planning approaches are often insufficient to adequately capture such complexity or to support robust operational decision-making. This paper examines the modelling and optimisation of complex maritime transport systems by integrating network-based optimisation and stochastic modelling approaches. The methodological framework combines operational research techniques with simulation in order to represent maritime transportation as a directed time–space network with stochastic demand and time windows. Uncertainty related to operational disturbances, port congestion, and variable demand is explicitly incorporated into the optimisation process. A case-oriented application demonstrates how stochastic network-based optimisation can improve routing, scheduling, and recovery strategies compared to purely deterministic approaches. The results indicate that controlled flexibility in arrival times and routing decisions leads to improved operational performance, enhanced resilience, and better trade-offs between fuel consumption, service reliability, and recovery costs. The proposed framework serves as a decision-support tool for maritime operations, providing structured analytical support, while preserving the role of human judgment in safety-critical environments. By bridging systems theory, operational research, and applied maritime modelling, the study contributes to the development of robust decision-support approaches for complex and uncertain maritime transport systems.
Nadrljanski et al. (Thu,) studied this question.