• Integrated framework linking demand, network planning, and competition. • Endogenous demand-supply optimization solved via scalable cutting-plane. • Scenario-based game-theoretic model captures hub competition under uncertainty. • Case study (Singapore) delivers market-level forecasts and strategic insights. The strategic planning of long-haul air transport networks is shaped by complex interdependencies among passenger demand, airline decisions, airport capacity, and competitive dynamics. Hub-based operations are central to ensuring the economic viability of long-haul routes, yet they introduce significant managerial and modeling challenges, especially under uncertainty and evolving market conditions. Amid these challenges, this paper introduces an integrated optimization framework designed to inform long-term strategic planning for hub-based long-haul operations. The framework unifies three key components: (i) a demand estimation model that jointly predicts total market demand and its allocation across itineraries using a global dataset of 2019 long-haul passenger flows; (ii) a network and frequency planning optimization model that captures endogenous demand-supply interactions, solved via a cutting-plane approach for scalability; and (iii) a scenario-based game-theoretic algorithm that accounts for strategic competition among hubs under macroeconomic volatility. We validate the framework through a case study of Singapore and its primary competitors in Southeast Asia, demonstrating its ability to replicate baseline traffic patterns and assess future network development scenarios through 2027. The results offer not only aggregate forecasts but also granular, market-level insights into expected growth trajectories and competitive pressures, underscoring the framework’s potential to support strategic decision-making by airlines, airports, and policymakers aiming to enhance long-haul connectivity and regional competitiveness.
Li et al. (Sat,) studied this question.