  Traditional round-trip car rental systems mandate users to return vehicles to
their point of origin, limiting the system adaptability to meet diverse
mobility demands. This constraint often leads to fleet under-utilization and
incurs high parking costs for idle vehicles. To address this inefficiency, we
propose a N-user matching algorithm which is designed to facilitate one-way
trips within the round-trip rental framework. Our algorithm addresses the joint
problem of optimal pricing and user matching through a Two-Stage Integer Linear
Programming (ILP)-based formulation. In the first stage, optimal rental prices
are determined by setting a risk factor that governs the likelihood of matching
a set of N-user. The second stage involves maximizing expected profit through a
novel ILP-based user-matching formulation. Testing our algorithm on real-world
scenarios demonstrates an approximate 35\% increase in demand fulfillment.
Additionally, we assess the model robustness under uncertainty by varying
factors such as the risk factor (probability of user ride acceptance at the
offered price), cost factor (rental cost-to-fare ratio), and maximum chain
length.
