本文主要是介绍（吐血整理）118篇强化学习求解车间调度文章（英文）大全，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
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在读博期间，由于课题也是与深度强化学习求解车间调度问题相关，所以一直关注这个领域的文章，我的博士论文第二章也做了详细的综述。最近也有不少粉丝在寻求强化学习求解车间调度的文章，所以就想着怎么给粉丝传递这些文章。这个领域发展速度实在太快，通过发表综述论文这种比较慢的方式着实难以满足粉丝的需求，所以还是希望通过公众号文章的形式分享给大家，包括endnote参考文献格式和原文，希望对大家有所帮助，吐血整理实属不易，多多转发，不胜感激。关注公众号，后台回复“综述”获取参考文献。
从1995年最早将强化学习用于车间调度问题后，在随后的几年里，强化学习一直不温不火，最主要的原因是一般的强化学习无法解决状态空间爆炸的问题，直到2018年深度强化学习开始进军调度领域，并在随后的几年里爆发式增长，在2022年上半年还未结束的情况下，已有11篇甚至更多的文章发表，可见这个方向的火热。一方面深度强化学习确实利用深度学习领域技术实现了未知状态下行为的预测，另一方面车间调度一直是悬而未决的经典问题，也是检验包括深度强化学习在内的各种算法的测试床。
下面是从最近几年开始整理的文章，供大家参考。
| 序号 | 年份 | 期刊or会议 | 文章 |
|---|---|---|---|
| 1 | 2022 | Sustainability | Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization |
| 2 | 2022 | Machines | Deep Reinforcement Learning for Distributed Flow Shop Scheduling with Flexible Maintenance |
| 3 | 2022 | Computers & Operations Research | Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm |
| 4 | 2022 | Journal of Intelligent Manufacturing | On reliability of reinforcement learning based production scheduling systems: a comparative survey |
| 5 | 2022 | International Journal of Production Research | Deep reinforcement learning for dynamic scheduling of a flexible job shop |
| 6 | 2022 | Robotics and Computer-Integrated Manufacturing | Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network |
| 7 | 2022 | Expert Systems with Applications | Deep reinforcement learning based scheduling within production plan in semiconductor fabrication |
| 8 | 2022 | Journal of Manufacturing Systems | Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning |
| 9 | 2022 | Autonomous Agents and Multi-Agent Systems | A practical guide to multi-objective reinforcement learning and planning |
| 10 | 2022 | Applied Sciences | Minimizing the Late Work of the Flow Shop Scheduling Problem with a Deep Reinforcement Learning Based Approach |
| 11 | 2022 | Processes | Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival |
| 12 | 2021 | Ieee Access | Reinforcement Learning With Composite Rewards for Production Scheduling in a Smart Factory |
| 13 | 2021 | Computers & Industrial Engineering | A cooperative water wave optimization algorithm with reinforcement learning for the distributed assembly no-idle flowshop scheduling problem |
| 14 | 2021 | Sensors (Basel) | Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning |
| 15 | 2021 | Journal of Manufacturing Systems | A fast decision-making method for process planning with dynamic machining resources via deep reinforcement learning |
| 16 | 2021 | 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) | Smart Scheduling for Flexible and Hybrid Production with Multi-Agent Deep Reinforcement Learning |
| 17 | 2021 | Computer Networks | Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning |
| 18 | 2021 | Complex System Modeling Simulation | A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling |
| 19 | 2021 | 17th IFAC Symposium on Information Control Problems in Manufacturing (INCOM) | Enabling adaptable Industry 4.0 automation with a modular deep reinforcement learning framework |
| 20 | 2021 | 23rd International Conference on Enterprise Information Systems (ICEIS) | Global Reward Design for Cooperative Agents to Achieve Flexible Production Control under Real-time Constraints |
| 21 | 2021 | International Journal of Production Research | Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning |
| 22 | 2021 | International Journal of Production Research | Deep reinforcement learning in production systems: a systematic literature review |
| 23 | 2021 | Ieee Transactions on Emerging Topics in Computational Intelligence | Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling |
| 24 | 2021 | Neural Computing and Applications | A Prioritized objective actor-critic method for deep reinforcement learning |
| 25 | 2021 | Journal of Intelligent Manufacturing | Modular production control using deep reinforcement learning: proximal policy optimization |
| 26 | 2021 | 17th IFAC Symposium on Information Control Problems in Manufacturing (INCOM) | A Deep Reinforcement Learning approach for the throughput control of a Flow-Shop production system |
| 27 | 2021 | Ieee Transactions on Automation Science and Engineering | Real-Time Scheduling for Dynamic Partial-No-Wait Multiobjective Flexible Job Shop by Deep Reinforcement Learning |
| 28 | 2021 | Computers & Industrial Engineering | Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning |
| 29 | 2021 | Mathematical Problems in Engineering | A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling |
| 30 | 2021 | International Journal of Production Research | Reinforcement learning for robotic flow shop scheduling with processing time variations |
| 31 | 2021 | Procedia CIRP | Dynamic scheduling in a job-shop production system with reinforcement learning |
| 32 | 2021 | Journal of Manufacturing Systems | Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning |
| 33 | 2021 | International Journal of Simulation Modelling | A Deep Reinforcement Learning Based Solution for Flexible Job Shop Scheduling Problem |
| 34 | 2021 | 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM) | Integration of the A2C Algorithm for Production Scheduling in a Two-Stage Hybrid Flow Shop Environment |
| 35 | 2021 | Applied Sciences-Basel | Intelligent Scheduling with Reinforcement Learning |
| 36 | 2021 | Computers & Industrial Engineering | Agent-based approach integrating deep reinforcement learning and hybrid genetic algorithm for dynamic scheduling for Industry 3.5 smart production |
| 37 | 2021 | International Journal of Production Research | End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning |
| 38 | 2020 | Proceedings of 2020 Ieee 9th Data Driven Control and Learning Systems Conference | A Deep Reinforcement Learning Approach to the Flexible Flowshop Scheduling Problem with Makespan Minimization |
| 39 | 2020 | Procedia Cirp | Deep reinforcement learning-based dynamic scheduling in smart manufacturing |
| 40 | 2020 | Advances in Neural Information Processing Systems | Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning |
| 41 | 2020 | International Journal of Production Research | Adaptive scheduling for assembly job shop with uncertain assembly times based on dual Q-learning |
| 42 | 2020 | AAS/AIAA Astrodynamics Specialist Conference | Using multi-objective deep reinforcement learning to uncover a pareto front in multi-body trajectory design |
| 43 | 2020 | International Journal of Production Research | Intelligent scheduling of discrete automated production line via deep reinforcement learning |
| 44 | 2020 | Icaart: Proceedings of the 12th International Conference on Agents and Artificial Intelligence, Vol 2 | Production Scheduling based on Deep Reinforcement Learning using Graph Convolutional Neural Network |
| 45 | 2020 | International Journal of Simulation Modelling | A NOVEL SOLUTION TO JSPS BASED ON LONG SHORT-TERM MEMORY AND POLICY GRADIENT ALGORITHM |
| 46 | 2020 | Ieee Transactions on Automation Science and Engineering | A Reinforcement Learning Approach to Robust Scheduling of Semiconductor Manufacturing Facilities |
| 47 | 2020 | 2020 Prognostics and Health Management Conference (PHM-Besanon) | Solving Permutation Flowshop Problem with Deep Reinforcement Learning |
| 48 | 2020 | Applied Soft Computing | Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning |
| 49 | 2020 | IEEE Access | Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems |
| 50 | 2020 | 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) | Adaptive Scheduling for Smart Shop Floor Based on Deep Q-Network |
| 51 | 2020 | 2020 Ieee 32nd International Conference on Tools with Artificial Intelligence | Solving Open Shop Scheduling Problem via Graph Attention Neural Network |
| 52 | 2020 | 2020 Winter Simulation Conference (WSC) | Integration of Deep Reinforcement Learning and Discrete-Event Simulation for Real-Time Scheduling of a Flexible Job Shop Production |
| 53 | 2020 | Journal of Manufacturing Systems | Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network |
| 54 | 2020 | Computers & Industrial Engineering | Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0 |
| 55 | 2020 | IEEE Access | Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN |
| 56 | 2020 | Computers & Industrial Engineering | A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem |
| 57 | 2019 | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) | Manufacturing Dispatching Using Reinforcement and Transfer Learning |
| 58 | 2019 | IEEE Access | Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning |
| 59 | 2019 | Ifac Papersonline | Practical Reinforcement Learning - Experiences in Lot Scheduling Application |
| 60 | 2019 | Ifac Papersonline | Closed-loop Rescheduling using Deep Reinforcement Learning |
| 61 | 2019 | Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation | A Multi-objective Reinforcement Learning Algorithm for JSSP |
| 62 | 2019 | Ieee Transactions on Industrial Informatics | Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network |
| 63 | 2019 | Procedia CIRP | Autonomous order dispatching in the semiconductor industry using reinforcement learning |
| 64 | 2019 | Investigacion Operacional | Optimization of heavily constrained hybrid-flexible flowshop problems using a multi-agent reinforcement learning approach |
| 65 | 2019 | 13th INTERNATIONAL CONFERENCE on OPERATIONS RESEARCH (ICOR 2018) | AN IMPROVEMENT OF REINFORCEMENT LEARNING APPROACH TO PERMUTATIONAL FLOW SHOP SCHEDULING PROBLEM |
| 66 | 2019 | 2019 Second International Conference on Artificial Intelligence for Industries (AI4I) | Multi-agent reinforcement learning for job shop scheduling in flexible manufacturing systems |
| 67 | 2018 | 2018 29th annual SEMI advanced semiconductor manufacturing conference (ASMC) | Deep reinforcement learning for semiconductor production scheduling |
| 68 | 2019 | Procedia CIRP | Optimization of global production scheduling with deep reinforcement learning |
| 69 | 2018 | Journal of Intelligent Manufacturing | Adaptive job shop scheduling strategy based on weighted Q-learning algorithm |
| 70 | 2018 | CIRP Annals | Reinforcement learning for adaptive order dispatching in the semiconductor industry |
| 71 | 2018 | Computers & Industrial Engineering | Real-time scheduling for a smart factory using a reinforcement learning approach |
| 72 | 2018 | 2018 Ieee Biennial Congress of Argentina | Automatic Generation of Rescheduling Knowledge in Socio-technical Manufacturing Systems using Deep Reinforcement Learning |
| 73 | 2018 | IEEE Access | Gantry Work Cell Scheduling through Reinforcement Learning with Knowledge-guided Reward Setting |
| 74 | 2017 | Computers & Industrial Engineering | A reinforcement learning approach to parameter estimation in dynamic job shop scheduling |
| 75 | 2017 | Revista Cubana de Ciencias Informáticas | Adapting a Reinforcement Learning Approach for the Flow Shop Environment with sequence-dependent setup time |
| 76 | 2017 | Applied Sciences | Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning |
| 77 | 2017 | IFAC-PapersOnLine | A distributed approach solving partially flexible job-shop scheduling problem with a Q-learning effect |
| 78 | 2016 | Journal of Intelligent Manufacturing | An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning |
| 79 | 2016 | 2016 Ieee 21st International Conference on Emerging Technologies and Factory Automation | Learning Adaptive Dispatching Rules for a Manufacturing Process System by Using Reinforcement Learning Approach |
| 80 | 2016 | Procedia CIRP | Optimized Adaptive Scheduling of a Manufacturing Process System with Multi-skill Workforce and Multiple Machine Types: An Ontology-based, Multi-agent Reinforcement Learning Approach |
| 81 | 2016 | International Journal of Production Research | Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem |
| 82 | 2015 | Proceedings of 2015 Ieee 20th Conference on Emerging Technologies & Factory Automation | A Centralized Reinforcement Learning Approach for Proactive Scheduling in Manufacturing |
| 83 | 2015 | Service Orientation in Holonic and Multi-agent Manufacturing | A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products |
| 84 | 2014 | Iowa State University | Policy based reinforcement learning approach Of Jobshop scheduling with high level deadlock detection |
| 85 | 2013 | Asia-Pacific Journal of Operational Research | Flow shop scheduling with reinforcement learning |
| 86 | 2012 | Computers & Operations Research | Minimizing mean weighted tardiness in unrelated parallel machine scheduling with reinforcement learning |
| 87 | 2012 | International Journal of Production Research | Intelligent dynamic control of stochastic economic lot scheduling by agent-based reinforcement learning |
| 88 | 2012 | Expert Systems with Applications | SmartGantt – An intelligent system for real time rescheduling based on relational reinforcement learning |
| 89 | 2012 | Journal of Intelligent Manufacturing | Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning |
| 90 | 2011 | European Journal of Operational Research | Semiconductor final test scheduling with Sarsa(λ,k) algorithm |
| 91 | 2011 | 2011 IEEE International Summer Conference of Asia Pacific Business Innovation and Technology Management | Reinforcement learning combined with radial basis function neural network to solve Job-Shop scheduling problem |
| 92 | 2011 | International Conference on Production Research | Flow-Shop Robotic Scheduling Problem with Collaborative Reinforcement Learning |
| 93 | 2011 | Learning and Intelligent Optimization | A Reinforcement Learning Approach for the Flexible Job Shop Scheduling Problem |
| 94 | 2010 | 2010 IEEE International Conference on Industrial Engineering and Engineering Management | Reinforcement learning based scheduling in semiconductor final testing |
| 95 | 2010 | 2010 IEEE International Conference on Automation and Logistics | Rule driven multi objective dynamic scheduling by data envelopment analysis and reinforcement learning |
| 96 | 2009 | International Journal of Advanced Manufacturing Technology | An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell |
| 97 | 2009 | 2009 Fifth International Conference on Natural Computation | Multi-agent Co-evolutionary Scheduling Approach Based on Genetic Reinforcement Learning |
| 98 | 2009 | Universität Osnabrück | Multi-Agent Reinforcement Learning Approaches for Distributed Job-Shop Scheduling Problems |
| 99 | 2007 | International Journal of Advanced Manufacturing Technology | Dynamic parallel machine scheduling with mean weighted tardiness objective by Q-Learning |
| 100 | 2007 | 2007 IEEE Symposium on Computational Intelligence in Scheduling | Scaling Adaptive Agent-Based Reactive Job-Shop Scheduling to Large-Scale Problems |
| 101 | 2007 | International Journal of Information Technology and Intelligent Computing | Adaptive Reactive Job-Shop Scheduling with Reinforcement Learning Agents |
| 102 | 2006 | Advanced Engineering Informatics | Reinforcement learning in a distributed market-based production control system |
| 103 | 2005 | Engineering Applications of Artificial Intelligence | Application of reinforcement learning for agent-based production scheduling |
| 104 | 2005 | Simulation Modelling Practice and Theory | A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problem |
| 105 | 2005 | Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on | Dynamic single machine scheduling using Q-learning agent |
| 106 | 2005 | IEEE Transactions on Automation Science and Engineering | Relative value function approximation for the capacitated re-entrant line scheduling problem |
| 107 | 2005 | Multi-Agent Systems and Applications Iv, Proceedings | Stochastic reactive production scheduling by multi-agent based asynchronous approximate dynamic programming |
| 108 | 2004 | 2004 Ieee Conference on Robotics, Automation and Mechatronics, Vols 1 and 2 | Composite rules selection using reinforcement learning for dynamic job-shop scheduling |
| 109 | 2004 | Robotics and Computer-Integrated Manufacturing | Learning policies for single machine job dispatching |
| 110 | 2004 | Applied Intelligence | Distributed Reinforcement Learning Control for Batch Sequencing and Sizing in Just-In-Time Manufacturing Systems |
| 111 | 2003 | Mississippi State University, M1 - Degree of Doctor of Philosophy | Application of reinforcement learning to multi-agent production scheduling |
| 112 | 2003 | Production Systems and Information Engineering | Flow-shop scheduling based on reinforcement learning algorithm |
| 113 | 2001 | 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479) | Reinforcement learning approach to re-entrant manufacturing system scheduling |
| 114 | 2000 | International Transactions in Operational Research | Learning scheduling control knowledge through reinforcements |
| 115 | 2000 | Robotics and Autonomous Systems | Dynamic job-shop scheduling using reinforcement learning agents |
| 116 | 1999 | IEEE SMC’99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028) | An application of reinforcement learning to manufacturing scheduling problems |
| 117 | 1999 | Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence | A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling |
| 118 | 1995 | Proc. of 14th Int. Joint Conf. on Artificial Intelligence | A Reinforcement Learning Approach to Job-Shop Schedulling |
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