This paper presents a novel neural network design that learns the heuristic for Large Neighborhood Search (LNS). LNS consists of a destroy operator and a repair operator that specify a way to carry out the neighborhood search to solve the Combinatorial Optimization problems. The proposed approach in this paper applies a Hierarchical Recurrent Graph Convolutional Network (HRGCN) as a LNS heuristic, namely Dynamic Partial Removal, with the advantage of adaptive destruction and the potential to search across a large scale, as well as the context-awareness in both spatial and temporal perspective. This model is generalized as an efficient heuristic approach to different combinatorial optimization problems, especially to the problems with relatively tight constraints. We apply this model to vehicle routing problem (VRP) in this paper as an example. The experimental results show that this approach outperforms the traditional LNS heuristics on the same problem as well. The source code is available at \href{https://github.com/water-mirror/DPR}{https://github.com/water-mirror/DPR}.
G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic Heuristic Design
Software College, Northeastern University（东北大学软件学院）；International Centre for Theoretical Physics Asia-Pacific, University of Chinese Academy of Sciences（中国科学院大学国际理论物理亚太中心）；Taiji Laboratory for Gravitational Wave Universe, University of Chinese Academy of Sciences（中国科学院大学太极引力波宇宙实验室）；Tsinghua University（清华大学）
Zuse Institute Berlin（柏林祖斯研究所）；Hochschule für Technik und Wirtschaft Berlin（柏林工程与经济应用技术大学）；Google Research（谷歌研究院）；Technische Universität Berlin（柏林工业大学）
Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems
Laboratoire d’Informatique (LIX), CNRS, École Polytechnique, Institut Polytechnique de Paris（信息实验室（LIX），法国国家科学研究中心，巴黎高等理工学院，巴黎理工学院）；Department of Computer Science and Engineering, Southern University of Science and Technology（计算机科学与工程系，南方科技大学）；School of Computing & Communications, Lancaster University Leipzig（计算与通信学院，莱斯特大学莱比锡分校）
Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Department of Economics and Management, University of Brescia（布雷西亚大学经济与管理学院）；Faculty of DS&AI, College of Technology, National Economics University（国家经济大学技术学院数据科学与人工智能学院）；CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematical and Industrial Engineering, Polytechnique Montreal（蒙特利尔理工学院数学与工业工程系 CIRRELT 及 SCALE-AI 数据驱动供应链讲席）
