From the 1 of 21 linked papers with an AI index.
6 papers · 1 filter
Guiding Large Language Models with Genetic Programming-Evolved Heuristic Knowledge for Dynamic Multi-Mode Project Scheduling
Yuan Tian, Yi Mei, Mengjie Zhang
The paper proposes using heuristic rules evolved by genetic programming to guide large language models in making dynamic multi-mode project scheduling decisions, improving performa…
Lifelong Learning with Behavior Consolidation for Vehicle Routing
Jiyuan Pei, Yi Mei, Jialin Liu +2
Recent neural solvers have demonstrated promising performance in learning to solve routing problems. However, existing studies are primarily based on one-off training on one or a s…
Investigation of the Generalisation Ability of Genetic Programming-evolved Scheduling Rules in Dynamic Flexible Job Shop Scheduling
Luyao Zhu, Fangfang Zhang, Yi Mei +1
Dynamic Flexible Job Shop Scheduling (DFJSS) is a complex combinatorial optimisation problem that requires simultaneous machine assignment and operation sequencing decisions in dyn…
Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling
Yuan Tian, Yi Mei, Mengjie Zhang
The dynamic multi-mode resource-constrained project scheduling problem is a challenging scheduling problem that requires making decisions on both the execution order of activities…
GAMA: A Neural Neighborhood Search Method with Graph-aware Multi-modal Attention for Vehicle Routing Problem
Xiangling Chen, Yi Mei, Mengjie Zhang
Recent advances in neural neighborhood search methods have shown potential in tackling Vehicle Routing Problems (VRPs). However, most existing approaches rely on simplistic state r…
DyRo-MCTS: A Robust Monte Carlo Tree Search Approach to Dynamic Job Shop Scheduling
Ruiqi Chen, Yi Mei, Fangfang Zhang +1
Dynamic job shop scheduling, a fundamental combinatorial optimisation problem in various industrial sectors, poses substantial challenges for effective scheduling due to frequent d…