A survey for solving mixed integer programming via machine learning
Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
Learning to solve large-scale security-constrained unit commitment problems
Security-constrained unit commitment (SCUC) is a fundamental problem in power systems
and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer …
and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer …
Deep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities
N Yang, C Yang, C **ng, D Ye, J Jia… - IET Generation …, 2022 - Wiley Online Library
This paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven
Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed …
Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed …
Accelerating process control and optimization via machine learning: A review
Process control and optimization have been widely used to solve decision-making problems
in chemical engineering applications. However, identifying and tuning the best solution …
in chemical engineering applications. However, identifying and tuning the best solution …
Taking the human out of decomposition-based optimization via artificial intelligence, Part II: Learning to initialize
The repeated solution of large-scale optimization problems arises frequently in process
systems engineering tasks. Decomposition-based solution methods have been widely used …
systems engineering tasks. Decomposition-based solution methods have been widely used …
Enhanced neural network-based attack investigation framework for network forensics: Identification, detection, and analysis of the attack
Network forensics aids in the identification of distinct network-based attacks through packet-
level analysis of collected network traffic. It also unveils the attacker's intentions and …
level analysis of collected network traffic. It also unveils the attacker's intentions and …
[HTML][HTML] Learning to handle parameter perturbations in combinatorial optimization: an application to facility location
We present an approach to couple the resolution of Combinatorial Optimization problems
with methods from Machine Learning. Specifically, our study is framed in the context where a …
with methods from Machine Learning. Specifically, our study is framed in the context where a …
Dmnet: A personalized risk assessment framework for elderly people with type 2 diabetes
Type 2 diabetes is the most common chronic disease for the elderly people. This disease is
difficult to be cured and causes continued medical expenses. The early and personalized …
difficult to be cured and causes continued medical expenses. The early and personalized …
Adaptive solution prediction for combinatorial optimization
This paper aims to predict optimal solutions for combinatorial optimization problems (COPs)
via machine learning (ML). To find high-quality solutions efficiently, existing work uses a ML …
via machine learning (ML). To find high-quality solutions efficiently, existing work uses a ML …
Identifying purchase intention through deep learning: analyzing the Q &D text of an E-Commerce platform
J Ma, X Guo, X Zhao - Annals of Operations Research, 2024 - Springer
Identifying purchase intention by analyzing the Query and the Document of the product
description (Q &D) text is one of the most important means of promoting Purchase Rate (PR) …
description (Q &D) text is one of the most important means of promoting Purchase Rate (PR) …