  This paper provides a novel framework for solving multiobjective discrete
optimization problems with an arbitrary number of objectives. Our framework
formulates these problems as network models, in that enumerating the Pareto
frontier amounts to solving a multicriteria shortest path problem in an
auxiliary network. We design techniques for exploiting the network model in
order to accelerate the identification of the Pareto frontier, most notably a
number of operations to simplify the network by removing nodes and arcs while
preserving the set of nondominated solutions. We show that the proposed
framework yields orders-of-magnitude performance improvements over existing
state-of-the-art algorithms on five problem classes containing both linear and
nonlinear objective functions.
