Abstract

The design and planning of systems subject to high-frequency time-varying conditions (e.g., prices, resource supplies, and customer demand) requires the solution of multi-period optimization problems, which have to account for operational aspects that are often described by complex nonlinear models. Accordingly, to overcome the computational challenges associated with the solution of the above problems, we present a framework to build computationally efficient and yet accurate optimization models. We also propose a general method to use trained piecewise linear (PWL) trees as surrogate models to approximate nonlinearities in relatively high dimensions and embed these trees onto mathematical optimization models. We show that, for some datasets, embedding PWL trees leads to models that result in a better balance between accuracy and computational performance when compared with approaches based on other machine-learning surrogate models. We showcase the applicability of the proposed framework via a case study on maintenance optimization of building cooling systems.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.