Data
Abstract
At present, the commonly used task scheduling methods of automated guided vehicle do not fully consider the influence of power consumption, the workshop environment, and other factors, resulting in the disparity between scheduling methods and practical applications. This article contributes to filling this gap by modifying the model and algorithm that can meet the real-time application in the factory. First, a scheduling model is established according to both the number of depots and the automated guided vehicle’s battery consumption, so that the result of task allocation is more reasonable. Then, according to the area, distribution, shape characteristics of obstacles, and the number of depots contained in the environment, this article derives a new coefficient which is constructed as the weighted value of the distance between workstations to improve the robustness of the model. Finally, the modified genetic algorithm is used to obtain the scheduling results. The simulation results show the effectiveness and the rationality of the proposed method.
Links
https://journals.sagepub.com/doi/10.1177/1729881419844152 https://doi.org/10.1177/1729881419844152