Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning

Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning

(2021) Junyoung Park et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Multi-agent reinforcement learning for online scheduling in smart factories

(2021) Tong Zhou et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

A semantic-level component-based scheduling method for customized manufacturing

(2021) Di Li et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Distributed scheduling problems in intelligent manufacturing systems

(2021) Yaping Fu et al.   TSINGHUA SCIENCE AND TECHNOLOGY

Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network

(2021) Yuxin Li et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

(2020) Shu Luo   APPLIED SOFT COMPUTING

Dynamic holding control to avoid bus bunching: A multi-agent deep reinforcement learning framework

(2020) Jiawei Wang et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management

(2020) Renzhi Lu et al.   APPLIED ENERGY

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

(2020) Martin Roesch et al.   Applied Sciences-Basel

MASK-RL: Multiagent Video Object Segmentation Framework Through Reinforcement Learning

(2020) Giuseppe Vecchio et al.   IEEE Transactions on Neural Networks and Learning Systems

Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

(2020) Huagang Liang et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Real-Time Scheduling of Cloud Manufacturing Services Based on Dynamic Data-Driven Simulation

(2019) Longfei Zhou et al.   IEEE Transactions on Industrial Informatics

A survey and critique of multiagent deep reinforcement learning

(2019) Pablo Hernandez-Leal et al.   AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS

Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment

(2019) Yuanjun Laili et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Scheduling in cloud manufacturing: state-of-the-art and research challenges

(2018) Yongkui Liu et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

A data mining approach for population-based methods to solve the JSSP

(2018) Mohammad Mahdi Nasiri et al.   SOFT COMPUTING

Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem

(2016) Nilsen Kundakcı et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

(2013) Min Dai et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

A novel multi-agent reinforcement learning approach for job scheduling in Grid computing

(2010) Jun Wu et al.   Future Generation Computer Systems-The International Journal of eScience

Applications of particle swarm optimisation in integrated process planning and scheduling

(2008) Y.W. Guo et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING