  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 $T$. This improves upon prior works achieving $\Omega(\sqrt{T})$
guarantees.
