Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine

PurposeThe transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face.Design/methodology/approachThis research proposes...

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Veröffentlicht in:	Industrial management + data systems Jg. 122; H. 3; S. 819 - 840
Hauptverfasser:	Lim, Ming K, Li, Yan, Wang, Chao, Tseng, Ming-Lang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Wembley Emerald Publishing Limited 15.03.2022 Emerald Group Publishing Limited
Schlagworte:	Air conditioners Air temperature Algorithms Artificial neural networks Cold storage Deviation Food Food quality Food waste Genetic algorithms Internet of Things Literature reviews Logistics Low temperature environments Machine learning Monitoring Neural networks Particle swarm optimization Performance enhancement Prediction models Predictive control Product safety Respiration Root-mean-square errors Route optimization Sensors Supply chains Temperature Temperature control Trucks Temperature prediction Cold chain logistics Mayfly algorithm Extreme learning machine
ISSN:	0263-5577, 1758-5783
Online-Zugang:	Volltext
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