Development of a sustainable strategy model for predicting optimal container stacking locations in container yards using artificial intelligence and cubic data

Abstract

Transporting containers advances the economy forward and hastens global transformation. Despite this swift change, ports face many difficulties, including how to store and extract containers. We provide a strategic, sustainable, dynamic model inspired by cubic data and artificial intelligence to solve the problem of container storage. This study creates a smart model based on cubic data and spatiotemporal data by integrating matrices into a multidimensional structure to overcome the uncertainty. The model proactively conjures data expressing yard components and examines what is compatible with or serves the situation it is trying to solve. This approach tries to bring together various concerned entities in the terminal yard, determine their properties and attributes, and process them in real-time. Moreover, the results of this study showed good agreement when compared with previous publications.