The likely large-scale deployment of autonomous vehicle (AV) technology in the near future has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. In this paper, we consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human drivers that join as individual contractors (ICs). Our operational models include (1) an open platform that outsources the high capital burden of AVs by allowing the AV supplier and human drivers to bring their own vehicles into the system, (2) an AV-only platform that is operated independently by the AV supplier, (3) a platform that sources AVs from the supplier through leasing contracts, and (4) an integrated supply chain in which the same entity operates the platform and supplies the AVs. We use (4) as a benchmark to measure the performances of the other models.

We study a classical problem in revenue management: quantity-based single-resource revenue management with no-shows. In this problem, a firm observes a sequence of T customers requesting a service. Each arrival is drawn independently from a known distribution of k different types, and the firm needs to decide irrevocably whether to accept or reject requests in an online fashion. The firm has a capacity of resources B, and wants to maximize its profit. Each accepted service request yields a type-dependent revenue and has a type-dependent probability of requiring a resource once all arrivals have occurred (or, be a no-show). If the number of accepted arrivals that require a resource at the end of the horizon is greater than B, the firm needs to pay a fixed compensation for each service request that it cannot fulfill. With a clairvoyant, that knows all arrivals ahead of time, as a benchmark, we provide an algorithm with a uniform additive loss bound, i.e., its expected loss is independent of B and T. This improves upon prior works achieving $Ømega(\sqrtT )$ guarantees.