Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in
control systems, robotics, Machine Learning (ML) and optimization taking into account the …

With the development of deregulated retail power markets, it is possible for end users
equipped with smart meters and controllers to optimize their consumption cost portfolios by …

This paper presents an overview of inverse optimization through a bibliometric approach.
The goal of this study was to discover research trends and knowledge growth in the field of …

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 recent years, scheduling optimization has been utilized in production systems. To
construct a suitable mathematical model of a production scheduling problem, modeling …

This paper presents a data-driven inverse optimization (IO) approach to recover the
marginal offer prices of generators in a wholesale energy market. By leveraging underlying …

The deepening of the penetration of renewable energy is challenging how power system
operators cope with their variability and uncertainty. The inherent flexibility of dispathchable …

The supplemental material contains five appendices. Appendix A details why moves 3 and
up provide comparatively little information (relative to move 2) about the subjects' acquisition …

Due to cost-push inflation, the trade-off between safety costs and risk prevention (safety) has
become difficult worldwide. Most companies experience the difficulty of safety cost overruns …