Assortment Optimization Under General Choice

By Vivek F. Farias, Srikanth Jagabathula, Devavrat Shah. 2011

52 citations recorded by the source corpus.

We consider the key operational problem of optimizing the mix of offered products to maximize revenues when product prices are exogenously set and product demand follows a general discrete choice model. The key challenge in making this decision is the computational difficulty of finding the best subset, which often requires exhaustive search. Existing approaches address the challenge by either deriving efficient algorithms for specific parametric structures or studying the performance of general-purpose heuristics. The former approach results in algorithms that lack portability to other structures; whereas the latter approach has resulted in algorithms with poor performance. We study a portable and easy-to-implement local search heuristic. We show that it efficiently finds the global optimum for the multinomial logit (MNL) model and derive performance guarantees for general choice structures. Empirically, it is better than prevailing heuristics when no efficient algorithms exist, and it is within 0.02% of optimality otherwise.

Subfields: Management Information Systems, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/aa7861b1a65c8c9bd4b78e6fd9330201296012f6

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In Management Information Systems

In Management Science and Operations Research

In Marketing

Citations in the corpus

Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters (2014, background): “Jagabathula et al. (2011) also focus on assortment optimization problems where there is a cardinality constraint on the offered assortment.”