Strategic Timing and Dynamic Pricing for Online Resource Allocation

This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogenous price and time sensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand-capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers’ time sensitivities. The optimal policy may feature strategic idlenexss (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers), and deliberate late-service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand—high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared with dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is history-independent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

On Revenue Management with Strategic Customers Choosing When and What to Buy

In practice, when thinking about their purchasing decisions, customers usually strategize along two dimensions: (1) when to buy and (2) what to buy. That is, they might delay a purchase in anticipation of future price reductions, and/or they might purchase cheaper substitutes. Despite this, the literature has thus far dealt exclusively with either one of the two extremes whereby one of the two strategic dimension is missing. For example, a large body of work has studied forward-looking customers strategizing on when to buy but has done so merely within models in which customers have no alternatives to choose from. Conversely, another large body of work on assortment optimization and choice modeling has studied customers who choose what to buy from multiple product offerings but acting myopically. This paper takes a first step toward analyzing dynamic pricing when customers are fully strategic and choose both when and what to buy. Using a novel decomposition approach for the underlying multidimensional mechanism design problem, the paper presents theoretical and numerical performance analyses of static pricing policies.