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<p>This paper presents a belief-based dynamic pricing model for airlines, developed using Baoding Liu’s uncertainty theory. The model is designed to assist pricing strategies in unstable market conditions where historical data are either scarce or unreliable. Unlike traditional stochastic and robust optimization techniques, which often fail under high uncertainty, the proposed approach incorporates expert knowledge into the pricing process, enhancing its adaptability and practical relevance. Two distinct models derived from uncertainty theory are analyzed theoretically and tested numerically. The results indicate that both models demonstrate strong resilience to sudden market shocks, such as those caused by global pandemics or geopolitical conflicts. Numerical simulations reveal that the first model generates the highest expected revenue at USD 12,741.82, followed by the second model at USD 10,064.19. These outcomes surpass the performance of stochastic optimization (USD 11,126.43) and robust optimization (USD 8,990.83). Overall, the findings suggest that uncertainty theory offers a more adaptable and behaviorally informed framework for dynamic pricing, significantly improving the responsiveness and effectiveness of airline revenue management systems in uncertain environments.</p>

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Airline pricing: an uncertain programming approach

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • Maryam Almasi,
  • Mehri Bagherian

摘要

This paper presents a belief-based dynamic pricing model for airlines, developed using Baoding Liu’s uncertainty theory. The model is designed to assist pricing strategies in unstable market conditions where historical data are either scarce or unreliable. Unlike traditional stochastic and robust optimization techniques, which often fail under high uncertainty, the proposed approach incorporates expert knowledge into the pricing process, enhancing its adaptability and practical relevance. Two distinct models derived from uncertainty theory are analyzed theoretically and tested numerically. The results indicate that both models demonstrate strong resilience to sudden market shocks, such as those caused by global pandemics or geopolitical conflicts. Numerical simulations reveal that the first model generates the highest expected revenue at USD 12,741.82, followed by the second model at USD 10,064.19. These outcomes surpass the performance of stochastic optimization (USD 11,126.43) and robust optimization (USD 8,990.83). Overall, the findings suggest that uncertainty theory offers a more adaptable and behaviorally informed framework for dynamic pricing, significantly improving the responsiveness and effectiveness of airline revenue management systems in uncertain environments.