The approximability of assortment optimization under ranking preferences
Journal
Operations Research
Subject
Management Science and Operations
Publishing details
Authors / Editors
Aouad A;Farias V;Levi R;Segev D
Biographies
Publication Year
2018
Abstract
The main contribution of this paper is to provide best-possible approximability bounds for assortment planning under a general choice model, where customer choices are modeled through an arbitrary distribution over ranked lists of their preferred products, subsuming most random utility choice models of interest. From a technical perspective, we show how to relate this optimization problem to the computational task of detecting large independent sets in graphs, allowing us to argue that general ranking preferences are extremely hard to approximate with respect to various problem parameters. These findings are complemented by a number of approximation algorithms that attain essentially best-possible factors, proving that our hardness results are tight up to lower-order terms. Surprisingly, our results imply that a simple and widely studied policy, known as revenue-ordered assortments, achieves the best possible performance guarantee with respect to the price parameters.
Keywords
Assortment optimization; Choice models; Hardness of approximation; Independent set; approximation algorithms
Available on ECCH
No
