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A recent business model, on-demand warehousing, enables warehouse owners with extra distribution capacity to rent it out for short periods, providing firms needing flexible network designs a new type of distribution capacity. In this paper, a heuristic is created to solve large scale instances of dynamic facility location models that optimize distribution networks over a multi-period planning horizon, simultaneously considering the selection of different warehouse types with varying capacity granularity, commitment granularity, access to scale, and cost structures. The heuristic iteratively solves selected single-period problems, creating a set of smaller subproblems that are then solved for multiple periods. Their decisions are combined to achieve feasible low-cost solutions, ensuring each customer’s demand point is covered for each period. A set of computational experiments recommends how heuristic settings should be set by industrial decision makers and illustrates the heuristic can generate high-quality solutions for large scale networks during long planning horizons and many decision periods. The heuristic can solve national-level instances with many customer demand points, candidate locations, different warehouse types and capacity levels and many periods. 

On-demand warehousing platforms match companies with underutilized warehouse and distribution capabilities with customers who need extra space or distribution services. These new business models have unique advantages, in terms of reduced capacity and commitment granularity, but also have different cost structures compared with traditional ways of obtaining distribution capabilities. This research is the first quantitative analysis to consider distribution network strategies given the advent of on-demand warehousing. Our multi-period facility location model – a mixed-integer linear program – simultaneously determines location-allocation decisions of three distribution center types (self-distribution, 3PL/lease, on-demand). A simulation model operationally evaluates the impact of the planned distribution strategy when various uncertainties can occur. Computational experiments for a company receiving products produced internationally to fulfil a set of regional customer demands illustrate that the power of on-demand warehousing is in creating hybrid network designs that more efficiently use self-distribution facilities through improved capacity utilization. However, the business case for on-demand warehousing is shown to be influenced by several factors, namely on-demand capacity availability, responsiveness requirements, and demand patterns. This work supports a firm’s use of on-demand warehousing if it has tight response requirements, for example for same-day delivery; however, if a firm has relaxed response requirements, then on-demand warehousing is only recommended if capacity availability of planned on-demand services is high. We also analyze capacity flexibility options leased by third-party logistics companies for a premium price and draw attention to the importance of them offering more granular solutions to stay competitive in the market. 

To close the gap between current distribution operations and today’s customer expectations, firms need to think differently about how resources are acquired, managed and allocated to fulfill customer requests. Rather than optimize planned resource capacity acquired through ownership or long- term partnerships, this work focuses on a specific supply-side innovation – on-demand distribution platforms. On-demand distribution systems move, store, and fulfill goods by matching autonomous suppliers' resources (warehouse space, fulfillment capacity, truck space, delivery services) to requests on-demand. On-demand warehousing systems can provide resource elasticity by allowing capacity decisions to be made at a finer granularity (at the pallet-level) and commitment (monthly versus yearly), than construct or lease options. However, such systems are inherently more complex than traditional systems, as well as have varying costs and operational structures (e.g., higher variable costs, but little or no fixed costs). New decision- supporting models are needed to capture these trade-offs.