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Found 6 papers, 1 papers with code
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).
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description.
Polynomial Optimization: Enhancing RLT relaxations with Conic Constraints
In this research, we investigate the strengthening of RLT relaxations of polynomial optimization problems through the addition of nine different types of constraints that are based on linear, second-order cone, and semidefinite programming to solve to optimality the instances of well established test sets of polynomial optimization problems.
Learning for Spatial Branching: An Algorithm Selection Approach
The use of machine learning techniques to improve the performance of branch-and-bound optimization algorithms is a very active area in the context of mixed integer linear problems, but little has been done for non-linear optimization.
Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows
The past decade has seen a rapid penetration of electric vehicles (EV) in the market, more and more logistics and transportation companies start to deploy EVs for service provision.
Optimization Problems for Machine Learning: A Survey
This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches.
