Revenue Management (RM) and Pricing have a long tradition of elegant mathematical formulations, typically as large-scale stochastic dynamic programs, see, for instance, Gallego and van Ryzin (1994), Talluri and van Ryzin (1998), or Talluri and van Ryzin (2004). These models capture, in full generality, the trade-offs among capacity, uncertainty, and customer choice that define RM. Yet, as already noted by Talluri and van Ryzin (1998), the resulting problems are often simply too large to solve exactly. The enduring challenge, therefore, lies not in formulating the “perfect” model but in extracting useful, implementable decision policies from systems that defy exact optimization.

This Special Issue on Revenue Management for Complex Systems brings together five contributions that embody precisely this philosophy. Each paper begins with a theoretically sound model that captures the richness of real-world complexity (multi-dimensional heterogeneity, dynamic decisions under uncertainty, coupled subsystems, and data-driven learning) but then forges a computational path that makes the model tractable and actionable. Although they operate across diverse domains from pricing consumer products to managing transportation, logistics, and mobility networks, they share a unifying spirit: to turn complexity into structure, and structure into insight.