Abstract: We consider robust hybrid beamfocusing schemes against statistical and norm-bounded positioning errors in order to improve a total system data rate in near-field communications. To this end, ...

Abstract: Industrial Internet applications require networks to guarantee deterministic end-to-end latency and zero packet loss at both the data link and network layers. Traditional best-effort ...

In this paper, single and multi-objective robust optimization of a microgrid (MG) including photovoltaic (PV) and wind turbine (WT) sources with battery storage has been performed in a radial 33-bus ...

I have long admired the style adopted by the deterministic optimization community (popularized by Dantzig). Every single problem has decision variables (typically vectors), an objective function to be ...

Global optimization problems where evaluation of the objective function is an expensive operation arise frequently in engineering, decision making, optimal control, etc. There exist two huge but ...

Formulation and solution of applicable optimization models, including linear, integer, nonlinear, and network problems. Efficient algorithmic methods and use of oomputer modeling languages and systems ...

What is robust design optimization (RDO), and is it better than standard optimization? In this customer case, we describe a multi-disciplinary optimization of a turbocharger compressor from Ford Motor ...

In the rapidly evolving landscape of AI-powered products, one of the most critical decisions facing product teams is choosing between deterministic and generative AI approaches—or finding the right ...