[HTML][HTML] Graph neural networks for job shop scheduling problems: A survey
Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems
Combinatorial Optimization (CO) problems are fundamentally important in numerous real-
world applications across diverse industries, characterized by entailing enormous solution …
world applications across diverse industries, characterized by entailing enormous solution …
Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem.
There has been growing interest in using online Reinforcement Learning (RL) for JSSP …
There has been growing interest in using online Reinforcement Learning (RL) for JSSP …
Take a step and reconsider: Sequence decoding for self-improved neural combinatorial optimization
The constructive approach within Neural Combinatorial Optimization (NCO) treats a
combinatorial optimization problem as a finite Markov decision process, where solutions are …
combinatorial optimization problem as a finite Markov decision process, where solutions are …
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Current methods for end-to-end constructive neural combinatorial optimization usually train
a policy using behavior cloning from expert solutions or policy gradient methods from …
a policy using behavior cloning from expert solutions or policy gradient methods from …
FOG: A Unified Framework for Federated Combinatorial Optimization on Graphs
With the revolutionary advancements in deep learning technologies, neural combinatorial
optimization (NCO) emerges as a promising field for solving complex combinatorial …
optimization (NCO) emerges as a promising field for solving complex combinatorial …
Offline reinforcement learning for job-shop scheduling problems
Recent advances in deep learning have shown significant potential for solving combinatorial
optimization problems in real-time. Unlike traditional methods, deep learning can generate …
optimization problems in real-time. Unlike traditional methods, deep learning can generate …
State Feature Design Framework on Job Shop Scheduling: Graph Attention and Transformer-based Reinforcement Learning
This paper presents three key contributions:(1) a systematic framework for state feature
design in scheduling-domain reinforcement learning through DE and PSVNR …
design in scheduling-domain reinforcement learning through DE and PSVNR …
A Monte Carlo Tree Search with Reinforcement Learning and Graph Relational Attention Network for Dynamic Flexible Job Shop Scheduling Problem
F Zhao, Y Jia - Available at SSRN 5110453 - papers.ssrn.com
The real-time dynamic flexible job shop scheduling problem (DFJSP) with the machine fault
and recovery condition is being studied to determine the rescheduling scheme when …
and recovery condition is being studied to determine the rescheduling scheme when …