Conference Agenda

Ports are central nodes in multimodal transport networks, linking maritime or inland waterway transport with rail and road-based hinterland connections. Within these nodes, rail shunting operations are operationally critical but difficult to plan digitally because transport orders, infrastructure states, locomotive availability, wagon movements, and timing information are often distributed across heterogeneous systems. Existing digital platforms in port logistics primarily support information exchange and process visibility, while less attention is given to the transformation of local operational states into executable shunting decisions under time, resource, and infrastructure constraints.

This paper presents a reference architecture developed within the KIRBI project, which investigates AI-supported shunting and dispatching processes in port rail operations. The architecture is currently being developed and prototyped in two German port railway contexts, Dortmunder Hafen and Hafen Hamm, and targets the transition from conceptual design toward prototypical implementation. The contribution focuses on the system-level integration required to couple operational data, digital infrastructure representation, analytical services, heuristic optimization, and dispatcher-oriented decision support.

The architecture follows a modular, event-driven design. Heterogeneous inputs such as transport orders, telematics or GPS data, train arrival notifications, master data on locomotives and wagons, infrastructure information, and camera-based observations are harmonized into a common operational state model. This state model represents the current planning situation, including available resources, pending tasks, relevant infrastructure elements, operational dependencies, and timing constraints. Analytical services then provide decision-relevant parameters for dispatching. In this context, AI-based components are used as supporting services, for example for estimating travel or availability times and for camera-based validation of wagon sequences. By replacing static planning assumptions with situation-dependent estimates, these services allow the heuristic optimizer to react to changing operational conditions such as delayed arrivals, varying resource availability, or deviations in wagon sequences.

The dispatching decision itself is generated by a heuristic optimization component. The current prototype uses a constructive planning logic with iterative improvement: initial locomotive-task sequences are derived from operational priorities and feasibility constraints and are subsequently refined with neighborhood-based adjustments. The optimizer considers task readiness, due times, shift structures, setup times, travel times, resource availability, operational dependencies, and return-to-base requirements. Generated proposals are presented through a decision-support interface where dispatchers can review, adjust, or reject suggested plans. Changed operational states, dispatcher feedback, and execution events can trigger repeated replanning and create a basis for future model calibration and empirical evaluation.

The architecture differs from generic port information systems by focusing on local, time-critical rail shunting decisions rather than inter-organizational information exchange alone. Its transferability is addressed through a separation between site-specific elements, such as infrastructure topology and local process rules, and reusable architectural components, such as data adapters, event-based communication, operational state modeling, analytical services, and heuristic dispatching logic. As the current development stage lies between concept and prototype, the paper does not claim quantitative performance improvements. Instead, it defines the architectural design rationale and an evaluation framework for subsequent empirical assessment, including indicators such as delay, empty movements, resource utilization, plan stability, dispatcher workload, and replanning responsiveness.