A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …
application on the multivariable prediction of IES parameters have mostly been carried out …
Opportunities for reinforcement learning in stochastic dynamic vehicle routing
There has been a paradigm-shift in urban logistic services in the last years; demand for real-
time, instant mobility and delivery services grows. This poses new challenges to logistic …
time, instant mobility and delivery services grows. This poses new challenges to logistic …
OMLT: Optimization & machine learning toolkit
The optimization and machine learning toolkit (OMLT) is an open-source software package
incorporating neural network and gradient-boosted tree surrogate models, which have been …
incorporating neural network and gradient-boosted tree surrogate models, which have been …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
[HTML][HTML] Formulating data-driven surrogate models for process optimization
Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …
research into data-driven modeling for mathematical optimization of process applications …
When deep learning meets polyhedral theory: A survey
In the past decade, deep learning became the prevalent methodology for predictive
modeling thanks to the remarkable accuracy of deep neural networks in tasks such as …
modeling thanks to the remarkable accuracy of deep neural networks in tasks such as …
Piecewise linear neural networks and deep learning
As a powerful modelling method, piecewise linear neural networks (PWLNNs) have proven
successful in various fields, most recently in deep learning. To apply PWLNN methods, both …
successful in various fields, most recently in deep learning. To apply PWLNN methods, both …
Data-driven optimization for process systems engineering applications
D Van De Berg, T Savage, P Petsagkourakis… - Chemical Engineering …, 2022 - Elsevier
Most optimization problems in engineering can be formulated as 'expensive'black box
problems whose solutions are limited by the number of function evaluations. Frequently …
problems whose solutions are limited by the number of function evaluations. Frequently …
Linear model decision trees as surrogates in optimization of engineering applications
Abstract Machine learning models are promising as surrogates in optimization when
replacing difficult to solve equations or black-box type models. This work demonstrates the …
replacing difficult to solve equations or black-box type models. This work demonstrates the …
[HTML][HTML] Mixed-integer optimisation of graph neural networks for computer-aided molecular design
T McDonald, C Tsay, AM Schweidtmann… - Computers & Chemical …, 2024 - Elsevier
ReLU neural networks have been modelled as constraints in mixed integer linear
programming (MILP), enabling surrogate-based optimisation in various domains and …
programming (MILP), enabling surrogate-based optimisation in various domains and …