Dynamic assortment planning and capacity allocation with logit substitution

Omid Arhami, Shirin Aslani and Masoud Talebian

Journal of Retailing and Consumer Services, 2024, vol. 76, issue C

Abstract: We consider dynamic assortment planning and capacity allocation over multiple periods, where the retailer learns the demand online and actively based on sales data. Our focus is on substitutable products inside a category, where customers' preferences follow multinomial logit, which is unknown to the retailer. This setting, unlike the existing stylish models in retail management, allows us to tackle the real-world problem where the retailer 1) is constrained by a total capacity and 2) replenishes the inventory. Considering a constrained capacity implies that stock-outs may occur, and our algorithm continuously detects and filters out the curtailed demand data to overcome this censorship. Despite the limited capacity, numerical results illustrate that our algorithm has a sublinear regret in various situations. Our analyses also show that the capacity constraint significantly affects the learning and profit of a retailer. Finally, we show that a lower variety in the assortment leads to better revenue when the capacity is small.

Keywords: Demand learning; Retail management; Inventory replenishment (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:76:y:2024:i:c:s0969698923003545

DOI: 10.1016/j.jretconser.2023.103603

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