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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Vydané v:	Industrial management + data systems Ročník 122; číslo 3; s. 819 - 840
Hlavní autori:	Lim, Ming K, Li, Yan, Wang, Chao, Tseng, Ming-Lang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Wembley Emerald Publishing Limited 15.03.2022 Emerald Group Publishing Limited
Predmet:	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
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