  We develop a generalized inverse optimization framework for fitting the cost
vector of a single linear optimization problem given multiple observed
decisions. This setting is motivated by ensemble learning, where building
consensus from base learners can yield better predictions. We unify several
models in the inverse optimization literature under a single framework and
derive assumption-free and exact solution methods for each one. We extend a
goodness-of-fit metric previously introduced for the problem with a single
observed decision to this new setting, and demonstrate several important
properties. Finally, we demonstrate our framework in a novel inverse
optimization-driven procedure for automated radiation therapy treatment
planning. Here, the inverse optimization model leverages an ensemble of dose
predictions from different machine learning models to construct a consensus
treatment plan that outperforms baseline methods. The consensus plan yields
better trade-offs between the competing clinical criteria used for plan
evaluation.
