A Machine Learning Approach for Energy-Efficient Intelligent Transportation Scheduling Problem in a Real-World Dynamic Circumstances

This paper provides a novel intelligent scheduling strategy for a real-world transportation dynamic scheduling case from an engine workshop of general motor company (GMEW), which is a key production line throughout the manufacturing process. In order to reduce the carbon emission in the scheduling p...

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Vydáno v:	IEEE transactions on intelligent transportation systems Ročník 24; číslo 12; s. 15527 - 15539
Hlavní autoři:	Mou, Jianhui, Gao, Kaizhou, Duan, Peiyong, Li, Junqing, Garg, Akhil, Sharma, Rohit
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Carbon Clustering Completion time Crossovers Dynamic scheduling Emissions Energy consumption energy-efficient Evolution fuzzy random chance-constrained programming Genetic algorithms Heuristic algorithms Inverse scheduling Job shop scheduling Machine learning Populations Production lines real-life application Scheduling Sociology Statistics Transportation Workshops
ISSN:	1524-9050, 1558-0016
On-line přístup:	Získat plný text
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