The Travelling Salesman Problem (TSP) is a classical NP-hard problem and has broad applications in many disciplines and industries. In a large scale location-based services system, users issue TSP queries concurrently, where a TSP query is a TSP instance with $n$ points. In the literature, many advanced TSP solvers are developed to find high-quality solutions. Such solvers can solve some TSP instances efficiently but may take an extremely long time for some other instances. Due to the diversity of TSP instances, it is well-known that there exists no universal best solver dominating all other solvers on all possible TSP instances. To solve TSP efficiently, in addition to developing new TSP solvers, it needs to find a per-instance solver for each TSP instance, which is known as the TSP solver selection problem. In this paper, for the first time, we propose a deep learning framework, \CTAS, for TSP solver selection in an end-to-end manner. Specifically, \CTAS exploits deep convolutional neural networks to extract informative features from TSP instances and involves data argumentation strategies to handle the scarcity of labeled TSP instances. Moreover, to support large scale TSP solver selection, we construct a challenging TSP benchmark dataset with 6,000 instances, which is known as the largest TSP benchmark. Our \CTAS achieves over 2$\times$ speedup of the average running time, comparing the single best solver, and outperforms the state-of-the-art statistical models.
LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers
School of Computer Science, Huazhong University of Science and Technology, China（华中科技大学计算机学院）；Department of Computer Science, Cornell University, United States（康奈尔大学计算机系）
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Nanyang Technological University（南洋理工大学）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Computer Science, University of Science & Technology of China（中国科学技术大学计算机学院）；School of Mathematical and Computer Sciences, Heriot-Watt University（赫瑞瓦特大学数学与计算机科学学院）；College of Computer, National University of Defense Technology（国防科技大学计算机学院）
Learning-Based TSP-Solvers Tend to Be Overly Greedy
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Mathematics Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
GELD: A Unified Neural Model for Efficiently Solving Traveling Salesman Problems Across Different Scales
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University（教育部符号计算与知识工程重点实验室、计算机科学与技术学院、吉林大学）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University（老年人积极生活卓越研究中 心（NTU-UBC合作）、南洋理工大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院、南洋理工大学）
DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem
School of Computer Science, Huazhong University of Science and Technology（华中科技大学计算机学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Chongqing School, University of Chinese Academy of Sciences（中国科学院大学重庆学院）；Southwest Hospital, Third Military Medical University（陆军军医大学西南医院）；First Affiliated Hospital of Army Medical University（陆军军医大学第一附属医院）
Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization
Universidad Politécnica de Madrid（马德里理工大学）；Information Processing and Telecomunications Center（信息处理与通信中心）；ETSI Telecomunicación（电信高等技术工程学院）；Grupo de Tratamiento de Imágenes（图像处理组）
Algorithm Selection as a Bandit Problem with Unbounded Losses
IDSIA / USI-SUPSI Istituto Dalle Molle di studi sull’intelligenza artificiale（IDSIA / USI-SUPSI 达莱莫莱人工智能研究所）；Università della Svizzera italiana (USI)（瑞士意大利语区大学）；University of Lugano（卢加诺大学）；TU Munich（慕尼黑工业大学）
Learning from Drivers to Tackle the Amazon Last Mile Routing Research Challenge
Amazon Web Services（亚马逊云科技）
Amazon Last Mile Routing Research Challenge的目标是将Amazon司机的真实体验整合到最佳路线规划和优化的解决方案中。本文提出了我们的方法，通过分层结合机器学习和传统的旅行商问题（TSP）求解器来应对这一挑战。我们的方法从这两个世界中获益。一方面，我们的方法通过从区域级别的历史路线中学习顺序概率模型来编码驾驶员的专业知识，其中每个区域包含一些包裹停靠站。然后，它使用一个单步的策略迭代方法，称为推出算法，以生成合理的区域序列从学习的概率模型采样。另一方面，我们的方法利用丰富的TSP文献中开发的成熟方法，有效地在每个区域内进行排序。这种结合的结果似乎是有希望的。我们的方法获得了$0.0374$的评估分数，这与官方挑战排行榜上的前三名球队所取得的成绩相当。此外，我们基于学习的方法适用于可能表现出超出本挑战范围的不同顺序模式的驾驶路线。我们的方法的源代码可在https://github.com/aws-samples/amazon-sagemaker-amazon-routing-challenge-sol上公开获得。
LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers
Beijing Key Laboratory of Fault-Tolerant Quantum Computing, Beijing Academy of Quantum Information Sciences, Beijing, China（北京容错量子计算重点实验室，量子信息科学北京市院，北京，中国）；Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, CAS, Beijing, China（北京凝聚态物理国家实验室，物理研究所，中国科学院，北京，中国）；Beijing Key Laboratory of Advanced Quantum Technology, Beijing, China（北京先进量子技术重点实验室，北京，中国）；Hefei National Laboratory, Hefei, China（合肥国家实验室，合肥，中国）
A New Constructive Heuristic driven by Machine Learning for the Traveling Salesman Problem
Dalle Molle Institute for Artificial Intelligence（达尔莫尔人工智能研究所）；Università della Svizzera Italiana（提契诺大学）；University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）
