Classification-based policies for the online on-demand warehousing problem

In a dynamic global economic landscape, logistics companies have to be able to respond quickly and flexibly to changes in demand. This is where the concept of On-Demand Warehousing (ODW) comes in; an emerging approach that promises to revolutionize the way companies manage their warehouse space. This approach allows companies with temporary excess capacity to offer their space to others, who want to cover short-term demand peaks. By this, this concept provides advantages over traditional models, such as dedicated storage facilities or long-term leasing. However, the dynamic nature of this system presents unique challenges, especially in terms of matching customer requests with available storage in real time. Unlike offline models, where future demands are known or estimated, the Online ODWP requires decisions to be made without prior knowledge of upcoming requests. Our work addresses online ODWP by proposing an innovative methodology that integrates Machine Learning methods with sequential stochastic optimization to enhance decision making processes in real time. In an extensive computational study, we show that the newly proposed approach outperforms state-of-the-art heuristics and yields near optimal solutions within very short run times. Detailed algorithmic analyses as well as managerial insights are derived. We, for instance, provide decision guidelines for platform providers facing acceptance or rejection decisions on dynamically arriving storage requests.