Fairplay Towage and Fraunhofer CML have reached an initial milestone in a development project that applies quantum computing to tugboat scheduling at the Port of Hamburg. After six months of work, the team says it has built the foundations for an optimisation approach that can allocate tugs to operational requirements while weighing economic, technical, and environmental factors, marking a transition from concept framing toward a model that can be tested against real port conditions.

 

Why Tug Dispatch Is a High Value Optimisation Problem

 

Tug scheduling in a major container and multipurpose port is a complex coordination challenge because dispatch decisions are constrained by constantly shifting variables. Tug availability depends on where each tug is positioned, when inbound and outbound ships arrive, weather and sea state, terminal turnaround timing, and competing harbour activities that create bottlenecks. Small inefficiencies in dispatch can cascade into berth delays, higher fuel consumption, and knock-on disruption across a port call, which makes scheduling a useful target for advanced optimisation methods.

 

How the Model Is Being Built and What It Uses

 

The project is using Soft Park software to support planning improvements through realistic modelling and analytical algorithms aimed at reducing fuel use and minimising delays. The team has worked on defining the parameters that determine what a feasible tug plan looks like, including the number of tugs in the pool, crew deployment needs, and key technical characteristics such as tug length, draught, and bollard pull, which affect which vessels can be served and under what conditions.

 

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Operational Constraints That Make the Model Closer to Reality

 

To avoid producing schedules that look optimal on paper but fail operationally, the model includes practical constraints such as crew rest periods and tug maintenance windows. Incorporating these elements is critical because harbour towage is shaped by labour rules, fatigue management, and maintenance routines that limit effective capacity, and these constraints often determine whether a schedule can be executed reliably during peak traffic.

 

What Comes Next and How Success Will Be Measured

 

The next phase focuses on keeping the model close to real-world operations while still delivering decision value for dispatchers. That will require continuous calibration to ensure inputs reflect port dynamics and that outputs remain interpretable and actionable rather than mathematically elegant but operationally unrealistic. The practical test will be whether the approach can reduce idle time, unnecessary repositioning, and avoidable delays, translating optimisation into measurable fuel savings and smoother vessel turnaround.

 

Funding and Stakeholder Support

 

The development work is being funded by Investitions and Forderank Hamburg and is supported by the Port of Hamburg, reflecting an interest in applying advanced computing methods to port logistics where incremental improvements can yield outsized operational and emissions benefits at a system level.