The adoption of new technologies on pipelaying vessels is closely linked to the demand for full automation of pipe handling operations. However, pipe handling systems are rarely addressed in research, and, to the best of our knowledge, no studies exist that describe comparable intralogistics systems with similar control structures and movement restrictions. As a result, there is no clear reference for how such automation can be realized. This study addresses this gap by developing a conceptual pipe routing system for the pipelaying vessel Solitaire, operated by Allseas, and implements it within a Discrete Event Simulation model of the pipe handling system. The model is used to evaluate multiple routing strategies under different pipe supply scenarios and to determine the most effective routing strategy for the case study. The results show that a heuristic approach to sequential decision-making, framed within a model-free Markov Decision Process, can automate pipelaying operations and lay the groundwork for future optimization through reinforcement learning.