37 citations · 37 across the 2 of their papers we have counts for
3 papers
PUSH: a primal heuristic based on Feasibility PUmp and SHifting
Giorgio Grani, Corrado Coppola, Valerio Agasucci
This work describes PUSH, a primal heuristic combining Feasibility Pump and Shifting. The main idea is to replace the rounding phase of the Feasibility Pump with a suitable adaptat…
An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents
Marta Monaci, Valerio Agasucci, Giorgio Grani
There is a growing interest in integrating machine learning techniques and optimization to solve challenging optimization problems. In this work, we propose a deep reinforcement le…
Solving the single-track train scheduling problem via Deep Reinforcement Learning
Valerio Agasucci, Giorgio Grani, Leonardo Lamorgese
Every day, railways experience disturbances and disruptions, both on the network and the fleet side, that affect the stability of rail traffic. Induced delays propagate through the…