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An improved deep Q-learning algorithm for a trade-off between energy consumption and productivity in batch scheduling

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成果类型:
期刊论文
作者:
Zheng, Xu;Chen, Zhen
通讯作者:
Chen, Z
作者机构:
[Zheng, Xu] Changsha Univ Sci & Technol, Sch Econ & Management, Changsha 410076, Hunan, Peoples R China.
[Zheng, Xu] Univ Tennessee, Inst Secure & Sustainable Environm, Knoxville, TN 37996 USA.
[Chen, Zhen] Skshu Paint Co Ltd, Fujian Key Lab Architectural Coating, Putian 351100, Fujian, Peoples R China.
[Chen, Zhen] Univ Sci & Technol China, Sch Management, Hefei 230026, Anhui, Peoples R China.
通讯机构:
[Chen, Z ] S
Skshu Paint Co Ltd, Fujian Key Lab Architectural Coating, Putian 351100, Fujian, Peoples R China.
Univ Sci & Technol China, Sch Management, Hefei 230026, Anhui, Peoples R China.
语种:
英文
关键词:
Sustainability;Energy consumption;Energy-efficient scheduling;Batch processing machine;Deep reinforcement learning
期刊:
Computers & Industrial Engineering
ISSN:
0360-8352
年:
2024
卷:
188
基金类别:
Scientific Research Fund of Hunan Provincial Education Department [23B0262]
机构署名:
本校为第一机构
院系归属:
经济与管理学院
摘要:
The single -batch machine, commonly found in industrial manufacturing, can concurrently process a group of jobs in variable -speed batches, leading to fluctuating levels of both energy consumption and processing time. Identifying an optimal balance between total energy consumption and makespan presents a challenge due to the intricate nature of their relationship in real -world scenarios. This study introduces a mixed -integer programming model designed to determine optimal machine operating states for diverse workloads, meeting customer requirements while concurrently achieving energy savings...

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