The On-Demand Warehousing Problem: A Taxonomic Review

The on-demand warehousing problem (ODWP) has gained increasing attention in supply chain and operations research as firms seek more flexible and responsive storage solutions. This paper provides a taxonomic review of ODWP-related studies published between 1999 and 2025, tracing key milestones in the development of the field and positioning the present work in relation to earlier surveys. Rather than repeating prior reviews, the analysis builds on them by organizing existing contributions into a coherent taxonomy that reflects how ODWP research has evolved over time. To this end, a structured literature search tailored for taxonomy development was conducted using Google Scholar, based on Boolean combinations of warehousing and optimization keywords. After removing duplicates and screening titles, abstracts, and full texts against predefined inclusion and exclusion criteria, a final set of 79 relevant publications was retained. Each study was classified according to the ODWP problem dimensions it addresses and the solution models it employs. The resulting classification identifies 43 distinct modeling approaches, with CPLEX- and mixed integer linear programming (MILP)–based formulations accounting for ~17% of the applied techniques. In contrast, many alternative approaches, such as heuristic or stochastic methods, appear only sporadically in the literature. The proposed taxonomy illustrates how different problem dimensions shape the modeling of ODWP and reveals a clear concentration around a limited set of optimization techniques. At the same time, it brings attention to several areas that remain underexplored, including multiperiod and dynamic formulations, tighter integration of demand and supply uncertainty, cross-docking operations, and emerging concepts such as shared or collaborative on-demand warehousing models. Together, these insights clarify both the current state of ODWP research and the opportunities for future investigation. By bringing these findings together, the review gives researchers and practitioners a clear reference for choosing appropriate modeling approaches and for guiding future work on flexible, on-demand warehousing and just-in-time (JIT) operations.