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A review of reinforcement learning based intelligent optimization for manufacturing scheduling
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
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
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 …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
Deep reinforcement learning based optimization algorithm for permutation flow-shop scheduling
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 …
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 …
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 …
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
Designing an effective and efficient end-to-end optimization framework with good
generalization for shop scheduling is an emerging topic in the informational manufacturing …
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
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 …
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 …
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
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 …
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 …
resources to address users' demands (workflows) quickly and effectively. However, resource …