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

Abstract

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.

IoT- Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine

“…A mayfly algorithm (MA) with an ELM fusion-based approach for the prediction of the temperature of a refrigerated truck is presented in [53]. The hidden layer biases and input weights of ELM are improved using the MA for the prediction of temperature by ELM.…”

Performance of cold chain logistics service providers in the fast-moving consumer goods industry in Nigeria: a systematic review

“…The studies emphasized the importance of employing advanced monitoring systems, such as remote temperature sensors and data loggers, to ensure continuous temperature monitoring in cold storage facilities this helps in identifying issues, such as equipment malfunctions or temperature fluctuations. Thus, Temperature predictive alert systems and prompt corrective actions are crucial to mitigate the risks associated with temperature deviations in cold chain logistics [87], [117], [118]. Furthermore, [46] argued that maintaining temperature compliance during loading, unloading, and transportation is vital to preserve the quality and safety of perishable FMCG products and to minimizes temperature fluctuations while reducing the risk of product spoilage.…”

Cold Chain Low-carbon Logistics Path Optimisation Method Based on Improved Hummingbird Optimisation Algorithm

“…The cold chain low-carbon logistics distribution path optimisation technique considers all the costs in the cold chain low-carbon logistics distribution process on the basis of meeting the maximum vehicle load, and solves the cold chain low-carbon logistics distribution path optimisation model with the minimum total cost based on the constraints of customer demand, vehicle load and time window [6]. Commonly used logistics path optimisation methods include exact optimisation algorithms and heuristic algorithms [7]. Logistics distribution methods based on exact optimisation algorithms can fall into dimensional explosion, making it difficult for the algorithm to be met with a satisfactory solution in a short period of time [8].…”