Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory ...

We aim to address this by developing an integrated optimization framework that leverages chance-constrained stochastic optimization methods to mitigate the uncertainties of renewable generation.

As the rapid development of natural-gas fired units (NGUs), power systems begin to rely more on a natural gas system to supply the primary fuel. On the other hand, natural gas system contingency might ...

Our analyses reveal that the optimization of gene product diversity can be highly constrained by the underlying genetic architecture, effective population size, gene expression levels, and pleiotropic ...

A PyTorch implementation of constrained optimization and modeling techniques Transparent Models: Glassbox models to provide increased interpretability and insights into your ML models.

Lack of manpower is often cited as the most pressing barrier to the adoption of multiunit or refinery-wide optimization, as more and more workers retire out of industry and plants scramble to hire and ...

The original multiobjective optimization program is reformulated through the well-established ϵ-constraint scalarization method, in which the vector of scalarization parameters is treated as a ...