Results 141 to 150 of about 201,578 (174)
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A Pareto-Based Discrete Jaya Algorithm for Multiobjective Carbon-Efficient Distributed Blocking Flow Shop Scheduling Problem

IEEE Transactions on Industrial Informatics, 2023
Carbon peaking and carbon neutrality, which are significant strategies for national sustainable development, have attracted enormous attention from researchers in the manufacturing domain.
Fuqing Zhao, H. Zhang, Ling Wang
semanticscholar   +1 more source

Energy-Efficient Iterative Greedy Algorithm for the Distributed Hybrid Flow Shop Scheduling With Blocking Constraints

IEEE Transactions on Emerging Topics in Computational Intelligence, 2023
With the global energy shortage, climate anomalies, environmental pollution becoming increasingly prominent, energy saving scheduling has attracted more and more concern than before.
Hao-Xiang Qin   +6 more
semanticscholar   +1 more source

LS-HH: A Learning-Based Selection Hyper-Heuristic for Distributed Heterogeneous Hybrid Blocking Flow-Shop Scheduling

IEEE Transactions on Emerging Topics in Computational Intelligence, 2023
As the development of economic globalization, the distributed manufacturing has become common in modern industries. The scheduling of production resources in multiple production centers becomes an emerging topic.
Zhongshi Shao, W. Shao, D. Pi
semanticscholar   +1 more source

Iterated Greedy Algorithms for Flow-Shop Scheduling Problems: A Tutorial

IEEE Transactions on Automation Science and Engineering, 2022
An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems.
Ziyan Zhao, Mengchu Zhou, Shixin Liu
semanticscholar   +1 more source

A Cooperative Memetic Algorithm With Learning-Based Agent for Energy-Aware Distributed Hybrid Flow-Shop Scheduling

IEEE Transactions on Evolutionary Computation, 2022
With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, distributed manufacturing systems have become emerging due to the development of globalization.
Jing-jing Wang, Ling Wang
semanticscholar   +1 more source

A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem

IEEE Transactions on Cybernetics, 2022
Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises.
Fuqing Zhao, Shilu Di, Ling Wang
semanticscholar   +1 more source

Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm

IEEE Transactions on Cybernetics, 2022
A distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed. It requires to optimally assign jobs to multiple distributed factories and, at the same time, sequence them.
Yuxia Pan, K. Gao, Zhiwu Li, N. Wu
semanticscholar   +1 more source

A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System

IEEE Transactions on Cybernetics, 2021
In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total
Fuqing Zhao, Ru Ma, Ling Wang
semanticscholar   +1 more source

A novel shuffled frog-leaping algorithm with reinforcement learning for distributed assembly hybrid flow shop scheduling

International Journal of Production Research, 2022
Distributed hybrid flow shop scheduling (DHFS) problem has attracted much attention in recent years; however, DHFS with actual processing constraints like assembly is seldom considered and reinforcement learning is hardly embedded into meta-heuristic for
Jingcao Cai   +3 more
semanticscholar   +1 more source

A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem

International Journal of Production Research, 2022
A reinforcement learning-driven brain storm optimisation idea (RLBSO) is proposed in this paper to solve multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem.
Fuqing Zhao   +5 more
semanticscholar   +1 more source

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