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Found 5 papers, 1 papers with code
A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System
Our work offers practical insights for operators and enriches the integration of reinforcement learning into dynamic rebalancing problems, paving the way for more intelligent and robust urban mobility solutions.
A machine learning framework for neighbor generation in metaheuristic search
Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space.
Design and Implementation of an Heuristic-Enhanced Branch-and-Bound Solver for MILP
Our Branch-and-Bound algorithm is effective on a small portion of the training data set, and it manages to find an incumbent feasible solution for an instance that we could not solve with the Diving heuristics.
Revisiting local branching with a machine learning lens
In this work, we study the relation between the size of the search neighborhood and the behavior of the underlying LB algorithm, and we devise a leaning based framework for predicting the best size for the specific instance to be solved.
Learning chordal extensions
As a first building block of the learning framework, we propose an on-policy imitation learning scheme that mimics the elimination ordering provided by the (classical) minimum degree rule.
