Price and Assortment Optimization for Reusable Resources

We develop techniques for price and assortment optimization in systems of reusable resources with heterogeneous customer preferences when customer arrival rates vary over time. Our results are applicable in both the case of a finite-time horizon and an infinite time horizon in which customer arrival rates vary periodically. In the assortment-only setting we develop a randomized policy attaining a constant-factor guarantee with respect to the optimal dynamic policy. Our proposed algorithm is computationally feasible and allows an operator to trade-off between theoretical guarantees and computational effort. We extend these results to the joint pricing and assortment problem however we also demonstrate that computing these policies can be computationally challenging in general. Despite this we demonstrate techniques to develop joint pricing and assortment strategies in practically relevant special cases. We further propose dynamic policies that in contrast to a greedy policy effective account for the value of resources over time. Our computational experiments based on real world parking bay utilization, demonstrate the importance of accounting for this value in making operational decisions