Simulation-Based Booking Limits for Airline Revenue Management

TLDR

Deterministic mathematical programming models dominate airline revenue management theory and practice, yet they fail to capture demand uncertainty, nesting, and the dynamic booking process, and a leg‑based decomposition cannot fully represent network effects. The paper proposes a new algorithm to address these shortcomings. The algorithm refines any nested booking‑limit policy by integrating stochastic gradient optimization with approximate dynamic programming to improve booking limits. Simulation experiments indicate that the algorithm can yield practically significant revenue gains.

Abstract

Deterministic mathematical programming models that capture network effects play a predominant role in the theory and practice of airline revenue management. These models do not address important issues like demand uncertainty, nesting, and the dynamic nature of the booking process. Alternatively, the network problem can be broken down into leg-based problems for which there are satisfactory solution methods, but this approach cannot be expected to capture all relevant network aspects. In this paper, we propose a new algorithm that addresses these issues. Starting with any nested booking-limit policy, we combine a stochastic gradient algorithm and approximate dynamic programming ideas to improve the initial booking limits. Preliminary simulation experiments suggest that the proposed algorithm can lead to practically significant revenue enhancements.