Shelf-space allocation model with demand learning

In this paper, we studied the shelf-space allocation problem (SSAP). It is quite common recently to implement product design during a selling season and drastically change assortment decisions based on shelf-space allocation in response to up-to-date demand observations. While there are many literatures related to SSAP, However, existing literature assume that the demand is stationary. In this paper, we propose a dynamical framework to make shelf-space display decisions, in which space elasticity and potential demand are sequentially estimated using the latest data containing display space and sales for each product.