Zobrazeno 1 - 10
of 245
pro vyhledávání: '"Cao, Zhiguang"'
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, espe
Externí odkaz:
http://arxiv.org/abs/2410.21066
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues -- their performance significantly deteriorates on clean instances with c
Externí odkaz:
http://arxiv.org/abs/2410.04968
Autor:
Goh, Yong Liang, Cao, Zhiguang, Ma, Yining, Dong, Yanfei, Dupty, Mohammed Haroon, Lee, Wee Sun
Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learning task. However, their efficacy has primarily been demonstrated on ent
Externí odkaz:
http://arxiv.org/abs/2408.03585
Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced cost
Externí odkaz:
http://arxiv.org/abs/2405.11198
Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop
Externí odkaz:
http://arxiv.org/abs/2405.01029
Autor:
Lin, Zhuoyi, Wu, Yaoxin, Zhou, Bangjian, Cao, Zhiguang, Song, Wen, Zhang, Yingqian, Jayavelu, Senthilnath
Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP variants. This paper proposes the cross-problem learning to assist heuri
Externí odkaz:
http://arxiv.org/abs/2404.11677
Autor:
Guo, Hongshu, Ma, Yining, Ma, Zeyuan, Chen, Jiacheng, Zhang, Xinglin, Cao, Zhiguang, Zhang, Jun, Gong, Yue-Jiao
Evolutionary algorithms, such as Differential Evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algori
Externí odkaz:
http://arxiv.org/abs/2403.02131
Autor:
Ma, Zeyuan, Guo, Hongshu, Chen, Jiacheng, Peng, Guojun, Cao, Zhiguang, Ma, Yining, Gong, Yue-Jiao
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer. However, these approaches exhibit inherent limitations, including low op
Externí odkaz:
http://arxiv.org/abs/2403.01131
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 meaningful topological structures of disjunctive graphs (DGs). This paper prop
Externí odkaz:
http://arxiv.org/abs/2402.17606
Autor:
Ye, Haoran, Wang, Jiarui, Cao, Zhiguang, Berto, Federico, Hua, Chuanbo, Kim, Haeyeon, Park, Jinkyoo, Song, Guojie
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language model
Externí odkaz:
http://arxiv.org/abs/2402.01145