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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Bibliographic Details
Published in:Industrial management + data systems Vol. 122; no. 3; pp. 819 - 840
Main Authors: Lim, Ming K, Li, Yan, Wang, Chao, Tseng, Ming-Lang
Format: Journal Article
Language:English
Published: Wembley Emerald Publishing Limited 15.03.2022
Emerald Group Publishing Limited
Subjects:
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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Summary: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 a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study.FindingsThe prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R2). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature.Research limitations/implicationsThe case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM.Originality/valueIn prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.
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ISSN:0263-5577
1758-5783
DOI:10.1108/IMDS-10-2021-0607