Abstract
Dynamic assortment optimization with demand learning is a fundamental task in data-driven revenue management research that requires a combination of techniques from operations research, optimization, and machine learning. In this chapter, we give an overview of research on data-driven dynamic assortment optimization when the underlying demand is governed by probabilistic choice models beyond the classical multinomial logit (MNL) choice model, thereby overcoming several limitations and drawbacks of the MNL model. We also mention interesting unsolved questions for future research.
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Notes
- 1.
In terms of either the worst-case regret or the Bayes regret. We shall adopt the worst-case regret formulation here as it is more popular.
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Acknowledgements
We would like to thank the editors for their invitation and helpful guidelines on the writing of this chapter. We would also like to thank Sentao Miao for his suggestions that greatly helped the writing of Sect. 10.4.
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Wang, Y., Zhou, Y. (2022). Dynamic Assortment Optimization: Beyond MNL Model. In: Chen, X., Jasin, S., Shi, C. (eds) The Elements of Joint Learning and Optimization in Operations Management. Springer Series in Supply Chain Management, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-031-01926-5_10
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