Kharazmi University International Journal of Supply and Operations Management 2383-1359 12 4 2025 11 01 Interaction of Flight Scheduling and Ticket Pricing: A Modern Data-Driven Approach Based on Distributionally Robust Optimization and Bi-Level Programming 524 546 2969 10.22034/ijsom.2025.110846.3431 EN Ali Papi School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Mir Saman Pishvaee School of Industrial Engineering, Iran University of Science and Technology 0000-0001-6389-6308 Hadi Sahebi Department of Industrial Engineering, Faculty of System and e-Commerce Engineering, Iran University of Science & Technology, Tehran, Iran Journal Article 2025 07 10 In airline planning, tactical decisions related to flight schedule design and fleet assignment play a pivotal role in enhancing operational efficiency and maximizing revenue. On the other hand, ticket pricing, directly influencing market share, is inherently affected by the tactical flight timetable, market uncertainties, and passenger choice behavior. To jointly optimize tactical scheduling decisions and ticket pricing policies, and create optimal interaction between them, this paper proposes a modern data-driven decision-making framework that blends Distributionally Robust Optimization (DRO) with Bi-Level Programming (BLP). In this framework, leveraging historical data and machine learning algorithms, a distributional ambiguity set is first constructed to model uncertainty within the DRO framework. The BLP formulation then captures the interaction between flight scheduling (upper level) and ticket pricing (lower level). Additionally, passengers’ choice behavior is incorporated using a Multinomial Logit (MNL) discrete choice model. To address the computational complexity, a column-and-constraint generation (CCG) algorithm is adopted, enabling model decomposition and enhancing computational efficiency. Finally, the proposed model and solution framework are validated through a case study and a series of numerical experiments. Numerical results demonstrate that, compared to classical approaches, the proposed framework significantly improves market share and airline revenue, ensures robustness against uncertainty and passenger behavior variability, and enhances computational tractability. http://www.ijsom.com/article_2969_246efd8c1ec613b21a0cee1879770a78.pdf