Academic Journal
Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model.
| Τίτλος: | Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model. |
|---|---|
| Συγγραφείς: | Najafi, Sajjad1 (AUTHOR) najafi@hec.fr, Jasin, Stefanus2 (AUTHOR) sjasin@umich.edu, Uichanco, Joline3 (AUTHOR) ju441@nyu.edu, Zhao, Jinglong4 (AUTHOR) jinglong@bu.edu |
| Πηγή: | Operations Research. Jul/Aug2026, Vol. 74 Issue 4, p1879-1891. 13p. |
| Θεματικοί όροι: | *Discrete choice models, *Consumer behavior, *Price level changes, *Mathematical optimization, Mixed integer linear programming |
| Περίληψη: | Traditional assortment models assume that consumers evaluate products independently of the alternatives available (i.e., the "context"). In "Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model," the authors challenge this assumption by analyzing assortment and pricing decisions under a context-dependent choice framework known as the contextual concavity (CC) model. The CC model incorporates reference dependence across multiple attributes, such as price and quality, and captures well-documented context effects, including compromise and decoy effects. The study makes several contributions. It characterizes the structure of optimal assortments under multiattribute loss aversion, develops a polynomial-size mixed-integer linear programming formulation for solving the general problem, and analyzes the joint assortment and pricing decision. Numerical experiments show that ignoring context effects, by relying on standard context-independent models such as the multinomial logit, can lead to substantial profit losses, with gaps ranging from 3% to 63%. These findings highlight the strategic importance of incorporating contextual effects into retail decisions. We study assortment and price optimization under the contextual concavity (CC) model introduced in the literature, which subsumes the well-known multiattribute loss aversion (MLA) model. Unlike context-independent choice models that assume product utilities are unaffected by other alternatives in the assortment, the CC model offers a context-dependent framework that incorporates reference points across multiple attributes and captures prominent context effects (e.g., the compromise effect) well documented in the empirical literature. We analytically characterize the structure of the optimal assortment in several settings and show that the pure assortment problem under the CC model can be reformulated as a mixed-integer linear program (MILP) that is polynomial in the number of products for a fixed number of attributes. For the joint assortment and pricing problem, we prove that the optimal assortment consists of all products, derive the structure of the optimal prices, and develop an approximation algorithm for computing a near-optimal solution. Finally, using MNL as a stylized benchmark, we conduct numerical experiments that provide illustrative evidence of how ignoring context effects may affect assortment decisions and profitability. Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2023.0377. [ABSTRACT FROM AUTHOR] |
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