Hopfield neural network based on clustering algorithms for solving green vehicle routing problem

As a result of the rapidly increasing distribution network, the toxic gases emitted by the vehicles to the environment have also increased, thus posing a threat to health. This study deals with the problem of determining green vehicle routes aiming to minimize CO2 emissions to meet customers' demand in a supermarket chain that distributes fresh and dried products. A new method based on clustering algorithms and Hopfield Neural Network is proposed to solve the problem. We first divide the large-size green vehicle routing problem into clusters using the K-Means and K-Medoids algorithms, and then the routing problem for each cluster is found using the Hopfield Neural Network, which minimizes CO2 emissions. A real-life example is carried out to illustrate the performance and applicability of the proposed method. The research concludes that the proposed approach produces very encroaching results. (C) 2022 by the authors; licensee Growing Science, Canada

Automated summary

Due to the expansion of the distribution network, the emission of toxic gases from vehicles has increased, posing a threat to the environment and health. This study proposes a new method based on clustering algorithms and Hopfield Neural Network to minimize CO2 emissions in the green vehicle routing problem for a supermarket chain. The results show that the proposed approach produces very satisfactory results.