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

Lim, M. K. , Li, Y., Wang, C. and Tseng, M.-L. (2022) Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine. Industrial Management and Data Systems, 12(3), pp. 819-840.

Abstract

Purpose: The 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/approach: This 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. Findings: The 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/implications: The 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/value: In 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.

• Item Type: Articles
• Additional Information: This research was funded by the National Natural Science Foundation of China (72071006 and 61603011).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Lim, M. K., Li, Y., Wang, C., and Tseng, M.-L.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Industrial Management and Data Systems
• Publisher: Emerald
• ISSN: 0263-5577
• ISSN (Online): 1758-5783
• Copyright Holders: Copyright © Emerald Publishing Limited
• First Published: First published in Industrial Management and Data Systems 12(3):819-840
• Publisher Policy: Reproduced in accordance with the publisher copyright policy