Related references
Note: Only part of the references are listed.Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing
Shengluo Yang et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2022)
Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints
Weibo Ren et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2022)
Multi-resource constrained dynamic workshop scheduling based on proximal policy optimisation
Peng Cheng Luo et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2022)
Adaptive scheduling for assembly job shop with uncertain assembly times based on dual Q-learning
Haoxiang Wang et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2021)
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning
Junyoung Park et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2021)
Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling
Fangfang Zhang et al.
IEEE TRANSACTIONS ON CYBERNETICS (2021)
Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning
Shu Luo
APPLIED SOFT COMPUTING (2020)
Adaptive job shop scheduling strategy based on weighted Q-learning algorithm
Yu-Fang Wang
JOURNAL OF INTELLIGENT MANUFACTURING (2020)
Intelligent scheduling of discrete automated production line via deep reinforcement learning
Daming Shi et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)
Real-time order dispatching for a fleet of autonomous mobile robots using multi-agent reinforcement learning
Andreja Malus et al.
CIRP ANNALS-MANUFACTURING TECHNOLOGY (2020)
Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm
Rui Zhang et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
A deep reinforcement learning approach for chemical production scheduling
Christian D. Hubbs et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
INTEGRATION OF DEEP REINFORCEMENT LEARNING AND DISCRETE-EVENT SIMULATION FOR REAL-TIME SCHEDULING OF A FLEXIBLE JOB SHOP PRODUCTION
Sebastian Lang et al.
2020 WINTER SIMULATION CONFERENCE (WSC) (2020)
A survey on machine learning in Internet of Things: Algorithms, strategies, and applications
Seifeddine Messaoud et al.
INTERNET OF THINGS (2020)
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems
Chien-Liang Liu et al.
IEEE ACCESS (2020)
Learning dispatching rules using random forest in flexible job shop scheduling problems
Sungbum Jun et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2019)
Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network
Chun-Cheng Lin et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation
Zelin Zang et al.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2019)
Deep Learning With Edge Computing: A Review
Jiasi Chen et al.
PROCEEDINGS OF THE IEEE (2019)
Autonomous order dispatching in the semiconductor industry using reinforcement learning
Andreas Kuhnle et al.
12TH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING (2019)
Design, Implementation and Evaluation of Reinforcement Learning for an Adaptive Order Dispatching in Job Shop Manufacturing Systems
Andreas Kuhnle et al.
52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS) (2019)
A Two-stage Genetic Programming Hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling
Fangfang Zhang et al.
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19) (2019)
Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems
Schirin Baer et al.
2019 SECOND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR INDUSTRIES (AI4I 2019) (2019)
Dynamic scheduling in modern processing systems using expert-guided distributed reinforcement learning
Shuhui Qu et al.
2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) (2019)
A survey of dispatching rules for the dynamic unrelated machines environment
Marko Durasevic et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints
Hegen Xiong et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2017)
On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions
Feng Liu et al.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT (2017)
A distributed approach solving partially flexible job-shop scheduling problem with a Q-learning effect
W. Bouazza et al.
IFAC PAPERSONLINE (2017)
Human-level control through deep reinforcement learning
Volodymyr Mnih et al.
NATURE (2015)
Distributed policy search reinforcement learning for job-shop scheduling tasks
Thomas Gabel et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2012)
A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions
Veronique Sels et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2012)
The study of real time scheduling by an intelligent multi-controller approach
Ruey-Shiang Guh et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2011)
LEARNING-BASED SCHEDULING OF FLEXIBLE MANUFACTURING SYSTEMS USING SUPPORT VECTOR MACHINES
Paolo Priore et al.
APPLIED ARTIFICIAL INTELLIGENCE (2010)
Training a neural network to select dispatching rules in real time
Wiem Mouelhi-Chibani et al.
COMPUTERS & INDUSTRIAL ENGINEERING (2010)
Learning effective new single machine dispatching rules from optimal scheduling data
Sigurdur Olafsson et al.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS (2010)
Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach
Yeou-Ren Shiue
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2009)
A survey of dynamic scheduling in manufacturing systems
Djamila Ouelhadj et al.
JOURNAL OF SCHEDULING (2009)
A neural network job-shop scheduler
Gary R. Weckman et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2008)
An effective architecture for learning and evolving flexible job-shop schedules
Nhu Binh Ho et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2007)
Application of reinforcement learning for agent-based production scheduling
YC Wang et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2005)
The complexity of decentralized control of Markov decision processes
DS Bernstein et al.
MATHEMATICS OF OPERATIONS RESEARCH (2002)