The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

This paper discusses a tri-layer non-cooperative energy trading approach among multiple
grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The …

The Springer Series in Operations Research and Financial Engineering publishes
monographs and textbooks on important topics in theory and practice of Operations …

Post-disruption restoration of critical infrastructures (CIs) often faces uncertainties associated
with the required repair tasks and the related transportation network. However, such …

We propose a novel, optimization-based method that takes into account the objective and
problem structure for reducing the number of scenarios, m, needed for solving two-stage …

Stochastic programming involves large-scale optimization with exponentially many
scenarios. This paper proposes an optimization-based scenario reduction approach to …

Scenario-based stochastic programming is a widely used method for optimization under
uncertainty. The solution quality of this approach is dependent on the approximation of the …

This paper presents strong scenario dominance cuts for effectively solving the multi-stage
stochastic mixed-integer programs (M-SMIPs), specifically focusing on the two most well …

Marketing demand response of industrial processes on electricity spot markets can reduce
operational cost. We apply our simultaneous day-ahead and intraday electricity market …