An application of stochastic dynamic programming to group revenue management

Abstract

Revenue Management (RM) is nowadays an essential tool used in large industries, especially by airline companies. This tool aims at optimising revenues by a better control of inventory and pricing among other factors. In this thesis, a stochastic optimality control problem which consists of finding an optimal policy to when it is profitable (or not) to accept a group request is considered. A detailed review of the literature available on optimal inventory control is given in the second chapter. However, very few references deal with the problem of group requests since these practices entail the computation, estimation and forecasting of several parameters and are often an integral part of the software used by the Revenue Management Department. For this reason, the required solution to the stochastic optimality control problem is obtained by the use of stochastic dynamic programming. More specifically, the object of study is described as a Markov Decision Problem (MDP) and solved using Reinforcement Learning (RL)