The vast majority of the machine scheduling literature focuses on deter-ministic problems, in which all data is known with certainty a priori. This may be a reasonable assumption when the variability in the problem parameters is low. However, as variability in the parameters increases incorporating this uncertainty explicitly into a scheduling model is… (More)
The vast majority of the machine scheduling literature focuses on deterministic problems in which all data is known with certainty a priori. In practice, this assumption implies that the random parameters in the problem are represented by their point estimates in the scheduling model. The resulting schedules may perform well if the variability in the… (More)
In this paper, we present an MIP formulation for the Unit Commitment problem that leads to significant computational time savings and almost-integral solutions when compared to the state-of-the-art formulations in the literature. Using a variety of test instances from the literature, we provide empirical evidence that the polyhedral structure of the new… (More)
In this study, we introduce a distribution network design problem that determines the locations and capacities of the relief distribution points in the last mile network, while considering demand-and network-related uncertainties in the post-disaster environment. The problem addresses the critical concerns of relief organizations in designing last mile… (More)
Conducting research on stochastic optimization, integer and disjunctive programming, statistical learning and prediction; working on applications, such as energy production planning problems. Worked on the development of relief network models; conducted analyses on a real-life case study.
Stochastic and integer programming, risk measures, applications to power systems operations, scheduling. Worked on the development and implementation of a solution method (integer L-shaped method) to the proposed stochastic programming models; analyzed the models and the solution method based on a case study.