Product-Closing Approximation for Ranking-based Choice Network Revenue Management

TL;DR

This paper introduces a new approximation method called the product closing program for ranking-based choice models in network revenue management, offering improved expected revenues and computational efficiency for large instances.

Contribution

The paper presents the product closing program, a novel approximation tailored for nonparametric ranking-based choice models, enhancing revenue estimation and solution quality.

Findings

The approach yields slightly higher expected revenues than existing methods.

It provides fast solutions suitable for large-scale problems.

The method can serve as an effective initial solution for other algorithms.

Abstract

Most recent research in network revenue management incorporates choice behavior that models the customers' buying logic. These models are consequently more complex to solve, but they return a more robust policy that usually generates better expected revenue than an independent-demand model. Choice network revenue management has an exact dynamic programming formulation that rapidly becomes intractable. Approximations have been developed, and many of them are based on the multinomial logit demand model. However, this parametric model has the property known as the independence of irrelevant alternatives and is often replaced in practice by a nonparametric model. We propose a new approximation called the product closing program that is specifically designed for a ranking-based choice model representing a nonparametric demand. Numerical experiments show that our approach quickly returns expected revenues that are slightly better than those of other approximations, especially for large instances. Our approximation can also supply a good initial solution for other approaches.