作业车间调度问题（JSSP）及其求解算法几十年来一直受到学术界和工业界的持续关注。近年来，机器学习（ML）在推进现有启发式方法以及构建新的启发式求解方法方面发挥着日益重要的作用，其目标是在更短的计算时间内找到更优的解。本文基于一种最先进的深度强化学习（DRL）智能体，即神经局部搜索（Neural Local Search，NLS），该智能体能够高效且有效地控制JSSP上的大规模局部邻域搜索。具体而言，我们提出了一种方法，在训练好的NLS智能体所产生的搜索轨迹上训练决策Transformer（Decision Transformer，DT）算法，以进一步改进所学习到的决策序列。实验表明，DT成功学习到了与NLS智能体自身不同、并且在许多情况下更为有效的局部搜索策略。在解质量与搜索所需可接受计算时间之间的权衡方面，DT在允许较长计算时间的应用场景中尤其具有优势。在这种情况下，尽管每一步搜索所需的推理时间更长（这是由更大的神经网络架构造成的），但DT通过每一步更高质量的决策弥补了这一不足。由此，DT在利用机器学习增强搜索求解JSSP方面取得了最先进的结果。
英文摘要：
The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML) is playing an increasingly important role in advancing existing and building new heuristic solutions for the JSSP, aiming to find better solutions in shorter computation times. In this paper we build on top of a state-of-the-art deep reinforcement learning (DRL) agent, called Neural Local Search (NLS), which can efficiently and effectively control a large local neighborhood search on the JSSP. In particular, we develop a method for training the decision transformer (DT) algorithm on search trajectories taken by a trained NLS agent to further improve upon the learned decision-making sequences. Our experiments show that the DT successfully learns local search strategies that are different and, in many cases, more effective than those of the NLS agent itself. In terms of the tradeoff between solution quality and acceptable computational time needed for the search, the DT is particularly superior in application scenarios where longer computational times are acceptable. In this case, it makes up for the longer inference times required per search step, which are caused by the larger neural network architecture, through better quality decisions per step. Thereby, the DT achieves state-of-the-art results for solving the JSSP with ML-enhanced search.
RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
School of Computing and Information Systems, Singapore Management University, Singapore（新加坡管理大学计算机与信息系）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）；Institute of Marine Science and Technology, Shandong University, China（山东大学海洋科学与技术研究院）
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）；Eindhoven University of Technology（埃因霍温理工大学）
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Intelligent Systems Science and Engineering, Jinan University（暨南大学智能系统科学与工程学院）
Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工学院应用科学学院）；Pazhou Lab (Huangpu), Guangzhou（广州 Pazhou 实验室（黄埔））；College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering（仲恺农业工程学院动物科学与技术学院）；Macao Polytechnic University（澳门理工学院）
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Nanyang Technological University（南洋理工大学）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）；National University of Singapore（新加坡国立大学）；Singapore Institute of Manufacturing Technology, A*STAR（新加坡科技研究局制造技术研究院）
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；Eindhoven University of Technology（埃因霍温理工大学）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）
Investigation of the Generalisation Ability of Genetic Programming-evolved Scheduling Rules in Dynamic Flexible Job Shop Scheduling
Centre for Data Science；Artificial Intelligence \& School of Engineering；Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Eindhoven University of Technology（埃因霍温理工大学）；Eindhoven Artificial Intelligence Systems Institute（埃因霍温人工智能系统研究所）；Delmia R&D, Dassault Systèmes（达索系统Delmia研发部）
DyRo-MCTS: A Robust Monte Carlo Tree Search Approach to Dynamic Job Shop Scheduling
Centre for Data Science & Artificial Intelligence（数据科学与人工智能中心）；School of Engineering & Computer Science（工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）
Policy-Based Reinforcement Learning with Action Masking for Dynamic Job Shop Scheduling under Uncertainty: Handling Random Arrivals and Machine Failures
South Westphalia University of Applied Sciences（西南弗劳恩霍夫应用科学大学）
Models and Strategies for Variants of the Job Shop Scheduling Problem
Cork Constraint Computation Centre（科克约束计算中心）；University College Cork（科克大学学院）；CNRS（法国国家科学研究中心）；LAAS（法国航空航天实验室）；Université de Toulouse（图卢兹大学）；UPS（图卢兹第三大学）；INSA（法国国立应用科学学院）；INP（法国国立理工学院）；ISAE（法国高等航空航天学院）；UT1（图卢兹第一大学）；UTM（图卢兹第二大学）
