The continuous roll-on roll-off dual cycling problem with tugs and driver-handled cargo units

Roll-on roll-off vessels are a popular mode of transport in short-sea shipping. In this domain, appropriate stevedoring procedures are crucial to enhance efficiency. This includes dual cycling, where tugs simultaneously load and unload the vessel. Dual cycling reduces turnaround time, thereby giving the vessel more time to travel and allowing for slow steaming and reduced emissions. We extend the roll-on roll-off dual cycling problem by incorporating a continuous time horizon and differentiating cargo units that are handled by their own drivers and units that must be handled by a tug. We propose a mixed integer linear programming model for generating an efficient schedule that minimizes overall makespan by optimizing the sequence of cargo units and the assignment of cargo units to tugs. To solve instances of real-world size with acceptable computational effort, we provide a range of heuristics, including a biased random-key genetic algorithm. Compared to the linear programming model and on instances of real-world size, the genetic algorithm finds good solutions quickly. We derive managerial insights from a sensitivity analysis and show that dual cycling and strategic positioning of driver-handled units can reduce turnaround time by 14.5%, reducing emissions of the considered vessel by more than 8%. We demonstrate the robustness of these insights in uncertain environments through a simulation study. • Speeding up a vessel’s turnaround enables slower steaming and reduces emissions. • In roll-on roll-off terminals, stevedoring is the main driver of turnaround time • Dual cycling significantly improves a terminal’s stevedoring performance. • A mixed integer linear programming model finds optimal schedules for small problem instances. • A biased-random key genetic algorithm efficiently finds good schedules for large instances.