Sales-target-oriented rule-based dynamic pricing system design for airlines

In today’s airline industry, the shift toward dynamic pricing—where prices evolve continuously rather than being fixed in fare classes—has become more than a trend; it represents a pivotal transformation in revenue management (RM). Yet, many existing systems still rely on legacy multi-fare-class reservation frameworks and conventional business logic, leaving them ill-equipped to handle modern demands. These systems often struggle with integrating competitive pricing data, capturing nuanced passenger behavior across web and mobile platforms, and reacting swiftly to real-time market shifts. More critically, they lack the capacity for processing the vast, granular datasets essential for artificial intelligence (AI)-powered revenue strategies. To bridge this gap, we developed a rule-based dynamic pricing algorithm that not only aligns with classic bid-price principles but also leverages both real-time data and the practical expertise of revenue managers. This algorithm is sales-target-driven and designed for responsiveness in live environments. Building on this foundation, we introduced a multi-layered software architecture tailored for airlines. Additionally, we offer practical recommendations for database structuring—both logical and physical—and propose streamlined business processes to enhance responsiveness under high computational loads and complex system integrations. Notably, a major Chinese airline has implemented our system prototype, marking a significant advancement in its RM capabilities.