The ordered weighted averaging (OWA) objective is an aggregate function over multiple optimization criteria that has received increasing attention by the research community over the last decade. Different to the weighted sum, where a certain weight is assigned to every objective function, weights are attached to ordered objective functions (i.e., for a fixed solution, objective functions are sorted with respect to their size, and weights are assigned to positions within this ordering). As this contains max-min or worst-case optimization as a special case, OWA can also be considered as an alternative approach to robust optimization. For linear programs with OWA objective, compact and extended reformulations exist. We present new such reformulation models with reduced size. A computational comparison indicates that these formulations improve solution times.