DRL4IPPS
DRL4IPPS focuses on utilizing Deep Reinforcement Learning to deal with Integrated Processing Planning and Scheduling Problem, which is an extremely hard realistic problem. The proposed method can make decisions within a few seconds and outperform traditional dispatching methods, as well as obtain an improvement of 11.35% compared with OR-Tools SAT-CP Solver and the Gurobi MILP Solver with a 7200-second time limit on large instances. Please refer to Paper (arXiv) for more details.
Apr 1, 2024
PDHCG-Net
PDHCG-Net aims to use Neural Networks, inspired by the updating rule of PDHCG, to warm start QP solvers. See PDHCG-Net for more details.
Dec 2, 2023
ML4MOC
ML4MOC is a benchmark for auto-selecting MIP Optimizer’s configuration.
Dec 2, 2023
LSTM-based Quasi-Newton
This project utlizes LSTM to approximate a precondition matrix inspired by quasi-newton method and use quasi-newton framework to optimize.
Nov 13, 2023