Train Unit Shunting is a complex process that directs trains through a shunting yard. In real-world railway operations, disturbances are common, requiring shunting schedules to be robust against uncertainties such as delays. Previous research has proposed algorithms for the Train Unit Shunting Problem (TUSP) and one study attempted to create robust shunting plans by defining a probabilistic model of the uncertainties involved and inferring a distribution of robust solutions for the TUSP. Following this approach, this paper investigates the use of probabilistic programming in increasing robustness of shunting plans using an advanced TUSP solver. This research develops a model for uncertain shunting scenarios, solves these scenarios, and applies importance sampling to infer the posterior distribution, producing a distribution of robust shunting plans instead of a single plan. The paper presents examples demonstrating that it is beneficial to use one of the output robust plans over the plan made for the deterministic scenario, revealing the potential of integrating probabilistic programming techniques into the planning process to improve railway efficiency and reduce delays.