  This chapter introduces the \emph{random-order model} in online algorithms.
In this model, the input is chosen by an adversary, then randomly permuted
before being presented to the algorithm. This reshuffling often weakens the
power of the adversary and allows for improved algorithmic guarantees. We show
such improvements for two broad classes of problems: packing problems where we
must pick a constrained set of items to maximize total value, and covering
problems where we must satisfy given requirements at minimum total cost. We
also discuss how random-order model relates to other stochastic models used for
non-worst-case competitive analysis.
