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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on intelligent transportation systems Vol. 24; no. 12; pp. 15527 - 15539
Main Authors:	Mou, Jianhui, Gao, Kaizhou, Duan, Peiyong, Li, Junqing, Garg, Akhil, Sharma, Rohit
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!