3 citations · 3 across the 4 of their papers we have counts for
4 papers
Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
Zhi Cao, Cong Zhang, Yaoxin Wu +2
The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assig…
Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization
Xia Jiang, Jing Chen, Cong Zhang +5
While large language models (LLMs) have shown strong performance in math and logic reasoning, their ability to handle combinatorial optimization (CO) -- searching high-dimensional…
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen +6
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the applicat…
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Cong Zhang, Zhiguang Cao, Yaoxin Wu +2
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningf…