A review of reinforcement learning based intelligent optimization for manufacturing scheduling

L Wang, Z Pan, J Wang - Complex System Modeling and …, 2021 - ieeexplore.ieee.org
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …

Machine and deep learning for resource allocation in multi-access edge computing: A survey

H Djigal, J Xu, L Liu, Y Zhang - IEEE Communications Surveys …, 2022 - ieeexplore.ieee.org
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …

Deep reinforcement learning based optimization algorithm for permutation flow-shop scheduling

Z Pan, L Wang, J Wang, J Lu - IEEE Transactions on Emerging …, 2021 - ieeexplore.ieee.org
As a new analogy paradigm of human learning process, reinforcement learning (RL) has
become an emerging topic in computational intelligence (CI). The synergy between the RL …

Task scheduling based on deep reinforcement learning in a cloud manufacturing environment

T Dong, F Xue, C **ao, J Li - Concurrency and Computation …, 2020 - Wiley Online Library
Cloud manufacturing promotes the transformation of intelligence for the traditional
manufacturing mode. In a cloud manufacturing environment, the task scheduling plays an …

An artificial neural network based approach for energy efficient task scheduling in cloud data centers

M Sharma, R Garg - Sustainable Computing: Informatics and Systems, 2020 - Elsevier
Energy efficiency is considered as a crucial objective in cloud data centers as it reduces cost
and meets the standard set in green computing. Task scheduling an important problem …

A knowledge-guided end-to-end optimization framework based on reinforcement learning for flow shop scheduling

Z Pan, L Wang, CX Dong… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Designing an effective and efficient end-to-end optimization framework with good
generalization for shop scheduling is an emerging topic in the informational manufacturing …

A survey of ai-enabled dynamic manufacturing scheduling: From directed heuristics to autonomous learning

J Ding, M Chen, T Wang, J Zhou, X Fu, K Li - ACM Computing Surveys, 2023 - dl.acm.org
As one of the most complex parts in manufacturing systems, scheduling plays an important
role in the efficient allocation of resources to meet individual customization requirements …

An energy-aware scheduling algorithm for budget-constrained scientific workflows based on multi-objective reinforcement learning

Y Qin, H Wang, S Yi, X Li, L Zhai - The Journal of Supercomputing, 2020 - Springer
Since scientific workflow scheduling becomes a major energy contributor in clouds, much
attention has been paid to reduce the energy consumed by workflows. This paper considers …

A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents

A Asghari, MK Sohrabi, F Yaghmaee - Computer Networks, 2020 - Elsevier
Cloud is a common distributed environment to share strong and available resources to
increase the efficiency of complex and heavy calculations. In return for the cost paid by cloud …

Deep reinforcement learning for fault-tolerant workflow scheduling in cloud environment

T Dong, F Xue, H Tang, C **ao - Applied Intelligence, 2023 - Springer
Cloud computing is widely used in various fields, which can provide sufficient computing
resources to address users' demands (workflows) quickly and effectively. However, resource …