Runway systems serve as the critical interface between airports and terminal airspace, and their efficient operation is essential for balancing air traffic demand and airport capacity. With the continuous growth of air traffic, intelligent runway demand and capacity management has become increasingly important for mitigating congestion and delays. This paper presents a comprehensive review of runway capacity–demand management from both supply-side and demand-side perspectives. On the supply side, runway configuration selection is reviewed, including runway configuration capacity envelopes, influencing factors, and existing optimization methodologies, such as prescriptive models, descriptive models, and reinforcement learning approaches. On the demand side, flight runway sequencing for arrivals, departures, and integrated arrival–departure operations is systematically analyzed. Problem analogies, operational characteristics, optimization objectives, and solution algorithms are discussed in detail. A critical comparison of existing methodologies is conducted from the perspectives of solution quality, real-time capability, human interpretability, technology readiness, trust requirements, and human–AI collaboration. Finally, future research directions are identified, including integrated runway management, multi-airport coordination, uncertainty-aware optimization, human–AI decision support, AI-enabled runway management, and integrated manned–unmanned operations. The review provides a reference for researchers, airport operators, air navigation service providers, and decision-support system developers seeking to improve runway operational efficiency and safety.
Jiang et al. (Thu,) studied this question.