[HTML][HTML] The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions
Abstract Container Terminals (CTs) are continuously presented with highly interrelated,
complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in …
complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in …
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 …
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 …
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 …
Reshaping national organ allocation policy
T Papalexopoulos, J Alcorn, D Bertsimas… - Operations …, 2024 - pubsonline.informs.org
The Organ Procurement & Transplantation Network (OPTN) initiated in 2018 a major
overhaul of all US deceased-donor organ allocation policies, aiming to gradually migrate …
overhaul of all US deceased-donor organ allocation policies, aiming to gradually migrate …
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
The rapid advancement of artificial intelligence (AI) techniques has opened up new
opportunities to revolutionize various fields, including operations research (OR). This survey …
opportunities to revolutionize various fields, including operations research (OR). This survey …
Encoding carbon emission flow in energy management: A compact constraint learning approach
Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible
climate change. Its basis is identifying emission responsibility during power allocation by the …
climate change. Its basis is identifying emission responsibility during power allocation by the …
Finding regions of counterfactual explanations via robust optimization
Counterfactual explanations (CEs) play an important role in detecting bias and improving
the explainability of data-driven classification models. A CE is a minimal perturbed data …
the explainability of data-driven classification models. A CE is a minimal perturbed data …
Optimizing over an ensemble of trained neural networks
K Wang, L Lozano, C Cardonha… - INFORMS Journal on …, 2023 - pubsonline.informs.org
We study optimization problems where the objective function is modeled through
feedforward neural networks with rectified linear unit (ReLU) activation. Recent literature has …
feedforward neural networks with rectified linear unit (ReLU) activation. Recent literature has …
Counterfactual explanations using optimization with constraint learning
To increase the adoption of counterfactual explanations in practice, several criteria that
these should adhere to have been put forward in the literature. We propose counterfactual …
these should adhere to have been put forward in the literature. We propose counterfactual …