This paper presents a multiobjective, mixed-integer linear programming (MILP) model that
integrates production scheduling, inventory management, and vessel assignment for a …

Inverse optimization—determining parameters of an optimization problem that render a
given solution optimal—has received increasing attention in recent years. Although …

Consider a problem where a set of feasible observations are provided by an expert, and a
cost function exists that characterizes which of the observations dominate the others and are …

Inverse linear programming (LP) has received increasing attention because of its potential to
infer efficient optimization formulations that can closely replicate the behavior of a complex …

In a typical (forward) optimization problem, a decision-maker uses given values of model
parameters to compute the values of decision variables. The goal in inverse optimization …

In radiation therapy treatment plan optimization, selecting a set of clinical objectives that are
tractable and parsimonious yet effective is a challenging task. In clinical practice, this is …

The objective in cancer radiotherapy is to maximize tumor-kill while limiting toxic effects of
radiation dose on nearby organs-at-risk (OAR). Given a fixed number of treatment sessions …

We consider the problem of inferring optimal solutions and unknown parameters of a
partially-known constrained problem using a set of observations or past decisions. We …

Inverse optimization involves recovering parameters of a mathematical model using
observed values of decision variables. In Markov Decision Processes (MDPs), it has been …

Conventional inverse optimization inputs a solution and finds the parameters of an
optimization model that render a given solution optimal. The literature mostly focuses on …