Warehousing platform’s revenue management: A dynamic model of coordinating space allocation for self-use and rent

Recently, some giant retail platforms have begun to invest large amounts of money in building warehouses. With abundant warehousing space, these platforms are renting out space to third-party retailers (TPRs) with insufficient storage space. However, such rental programs generate a new operational problem for these platforms, namely, how to divide their space between space for self-use and space for rent. To address the optimal space allocation decisions for this operational problem, we study a finite horizon and periodic review warehouse model where some items are sold by the platform, and other items are sold by TPRs. More specifically, we start with a base model with two items and prove the optimality of a base stock policy for the base model and the monotonicity of the optimal space allocation decisions regarding several key system parameters (such as inventory and capacity parameters). We then study several extensions of the base model by considering demand forecasts, demand correlation, and multiple items (more than two). Particularly, when addressing the extension with multiple items, we propose a heuristic based on the idea of approximate dynamic programming to overcome the curse of dimensionality. To examine the effectiveness of our heuristic, we present a computational study that is based on real-life datasets provided by the largest furniture retail platform in China (Red-Star Macalline). The numerical results demonstrate that our heuristic has the potential to solve medium- and large-scale problems effectively and efficiently.