Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template does not certify that the paper has been accepted for publication in the named... more
Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template does not certify that the paper has been accepted for publication in the named journal. INFORMS journal templates are for the exclusive purpose of submitting to an INFORMS journal and should not be used to distribute the papers in print or online or to submit the papers to another publication. We consider the single-period joint assortment and inventory planning problem with stochastic demand and dynamic substitution across products, motivated by applications in highly differentiated markets, such as online retailing and airlines. This class of problems is known to be computationally prohibitive. In fact, prior to the present paper, only a handful of modeling approaches were known to admit provably-good algorithms, at the cost of strong restrictions on customers' choice outcomes. Our main contribution is to provide the first efficient algorithms with provable performance guarantees for a broad class of dynamic assortment optimization models. Under general random-utility choice models, our approximation guarantee is order optimal with respect to the price parameters. We obtain improved guarantees under more specialized choice models, where customers' purchasing behaviors are elicited by price and quality cues. Our algorithms are myopic in nature, faster than existing heuristics by an order of magnitude and substantially increase revenue in extensive synthetic experiments. Technically speaking, we introduce a number of novel algorithmic ideas of independent interest, and unravel hidden relations to submodular maximization.
