The efficacy of Demand-Responsive Transit (DRT) is critically dependent on its ability to perform efficient routing amidst inherent passenger demand uncertainty. This chapter presents a conservatively tuned optimization framework that embeds adjustable risk parameters into the DRT planning process. We demonstrate that proactively hedging against demand ambiguity outperforms deterministic plans when actual demand deviates from forecasts. First, we establish a conceptual model framing DRT routing as a sequential decision-making process under uncertainty. We then analyze predominant routing paradigms—from myopic re-optimization to stochastic planning—and translate them into rigorous mathematical formulations. A dedicated case study on DRT service design underscores the practical importance of this approach, illustrating how robust optimization, by explicitly incorporating demand uncertainty and operator risk aversion, yields more resilient and profitable service plans compared to deterministic models that often overestimate performance.